1 Dominican Peso = 0.0667 United Arab Emirates Dirham

Billy Mills (Track & Field) 1953-57
Mills grew up on the Pine Ridge Indian Reservation for the Oglala Lakota Tribe in Pine Ridge, S.D. Growing up Mills participated in boxing and running but did not hone his skills on the track until he came to Lawrence, Kan., and Haskell Institute. Following his time at Haskell, the South Dakota native went onto star at the University of Kansas, where he was a three-time All-American and a Big 8 champion. Aside from his collegiate prowess, Mills did exceptionally well on the international stage, winning Gold in the 10,000 meters during the 1964 Olympics in Tokyo, where he became only the second Native American to capture Gold. The heralded Olympian continued to run after his Tokyo experience, breaking U.S. records in two events (10,000 meters and three mile run), as well as a world record in the six mile. Mills currently lives in Sacramento, Calif., where he is a spokesperson for ‘Running Strong for American Indian Youth' organization. He is also a member of numerous Hall of Fames throughout the nation, including the U.S. Olympic Hall of Fame as well as the National Distance Running Hall of Fame.

1 Papua New Guinean Kina = 5.4019 Namibian Dollar

1 Papua New Guinean Kina = 16.045 Dominican Peso

1 Papua New Guinean Kina = 2.0622 Chinese Yuan Renminbi

1 Papua New Guinean Kina = 1.0708 United Arab Emirates Dirham

1 Brunei Dollar = 13.1118 Namibian Dollar

1 Brunei Dollar = 38.9457 Dominican Peso

1 Brunei Dollar = 5.0056 Chinese Yuan Renminbi

1 Brunei Dollar = 2.5991 United Arab Emirates Dirham

Differentiating good IP from mediocre or bad IP is getting more difficult, in part because it depends upon how and where it is used and in part, because even the best IP may work better in one system than another—even in chips developed by the same vendor.  

So, how do you measure IP quality and why it is so complicated?

The answer depends on who is asking. Most of the time, the definition of IP quality depends on your vantage point.  If you are an R&D manager, IP quality means something. If you are a global supply manager, IP quality means something else. If you are an SoC start-up, your measure of quality is quite different from that of an established fabless company. If you are designing IP in-house, then your considerations are very different than being a commercial IP vendor. If you are designing an automotive SoC, then we are in a totally different category. How about as an IP vendor? How do you articulate IP quality metrics to your customers?

This varies greatly by the type of IP, as well. When it comes to interface (hard) IP and controllers, if you are an R&D manager, your goal is to design IP that meets the IP specifications and PPA (power, performance, and area) targets. You need to validate your design via silicon test chips. This applies to all hard PHYs, which must be mapped to a particular foundry process. For controllers that are in RTL form—we called these soft IP—you have to synthesize them into a particular target library in a particular foundry process in order to realize them in a physical form suitable for SoC integration. Of course, your design will need to go through a series of design validation steps via simulation, design verification and passing the necessary DRC checks, etc. In addition, you want to see the test silicon in various process corners to ensure the IP is robust and will perform well under normal process variations in the production wafers.

For someone in IP procurement, the measure of quality will be based on the maturity of the IP. This involves the number of designs that have been taped out using this IP and the history of bug reports and subsequent fixes. You will be looking for quality of the documentation and the technical deliverables. You will also benchmark the supplier’s standard operating procedures for bug reporting and technical support, as well as meeting delivery performance in prior programs. This is in addition to the technical teams doing their technical diligence.

An in-house team that is likely to design IP for a particular SoC project will be using an established design flow and will have legacy knowledge of last generation’s IP. They may be required to design the IP with some reusability in mind for future programs. However, such reusability requirements will not need to be as stringent and as broad as those of commercial IP vendors because there are likely to be established metrics and procedures in place to follow as part of the design team’s standard operating procedures. Many times, new development based on a prior design that has been proven in use will be started, given this stable starting point. All of these criteria help the team achieve a quality outcome more easily.

Then, if designing for an automotive SoC, additional heavy lifting is required.  Aside from ensuring that the IP meets the specifications of the protocol standards and passes the compliance testing, you also must pay attention to meeting functional safety requirements. This means adherence to ISO 26262 requirements and subsequently achieving ASIL certification. Oftentimes, even for IP, you must perform some AEC-Q100-related tests that are relevant to IP, such as ESD, LU, and HTOL.

To read more, please visit: https://semiengineering.com/why-ip-quality-is-so-difficult-to-determine/

This is the 15th installment of The Rationalist, my column for the Times of India.

It’s election season, and promises are raining down on voters like rose petals on naïve newlyweds. Earlier this week, the Congress party announced a minimum income guarantee for the poor. This Friday, the Modi government released a budget full of sops. As the days go by, the promises will get bolder, and you might feel important that so much attention is being given to you. Well, the joke is on you.

Every election, HL Mencken once said, is “an advance auction sale of stolen goods.” A bunch of competing mafias fight to rule over you for the next five years. You decide who wins, on the basis of who can bribe you better with your own money. This is an absurd situation, which I tried to express in a limerick I wrote for this page a couple of years ago:

POLITICS: A neta who loves currency notes/ Told me what his line of work denotes./ ‘It is kind of funny./ We steal people’s money/And use some of it to buy their votes.’

We’re the dupes here, and we pay far more to keep this circus going than this circus costs. It would be okay if the parties, once they came to power, provided good governance. But voters have given up on that, and now only want patronage and handouts. That leads to one of the biggest problems in Indian politics: We are stuck in an equilibrium where all good politics is bad economics, and vice versa.

For example, the minimum guarantee for the poor is good politics, because the optics are great. It’s basically Garibi Hatao: that slogan made Indira Gandhi a political juggernaut in the 1970s, at the same time that she unleashed a series of economic policies that kept millions of people in garibi for decades longer than they should have been.

This time, the Congress has released no details, and keeping it vague makes sense because I find it hard to see how it can make economic sense. Depending on how they define ‘poor’, how much income they offer and what the cost is, the plan will either be ineffective or unworkable.

The Modi government’s interim budget announced a handout for poor farmers that seemed rather pointless. Given our agricultural distress, offering a poor farmer 500 bucks a month seems almost like mockery.

Such condescending handouts solve nothing. The poor want jobs and opportunities. Those come with growth, which requires structural reforms. Structural reforms don’t sound sexy as election promises. Handouts do.

A classic example is farm loan waivers. We have reached a stage in our politics where every party has to promise them to assuage farmers, who are a strong vote bank everywhere. You can’t blame farmers for wanting them – they are a necessary anaesthetic. But no government has yet made a serious attempt at tackling the root causes of our agricultural crisis.

Why is it that Good Politics in India is always Bad Economics? Let me put forth some possible reasons. One, voters tend to think in zero-sum ways, as if the pie is fixed, and the only way to bring people out of poverty is to redistribute. The truth is that trade is a positive-sum game, and nations can only be lifted out of poverty when the whole pie grows. But this is unintuitive.

Two, Indian politics revolves around identity and patronage. The spoils of power are limited – that is indeed a zero-sum game – so you’re likely to vote for whoever can look after the interests of your in-group rather than care about the economy as a whole.

Three, voters tend to stay uninformed for good reasons, because of what Public Choice economists call Rational Ignorance. A single vote is unlikely to make a difference in an election, so why put in the effort to understand the nuances of economics and governance? Just ask, what is in it for me, and go with whatever seems to be the best answer.

Four, Politicians have a short-term horizon, geared towards winning the next election. A good policy that may take years to play out is unattractive. A policy that will win them votes in the short term is preferable.

Sadly, no Indian party has shown a willingness to aim for the long term. The Congress has produced new Gandhis, but not new ideas. And while the BJP did make some solid promises in 2014, they did not walk that talk, and have proved to be, as Arun Shourie once called them, UPA + Cow. Even the Congress is adopting the cow, in fact, so maybe the BJP will add Temple to that mix?

Benjamin Franklin once said, “Democracy is two wolves and a lamb voting on what to have for lunch.” This election season, my friends, the people of India are on the menu. You have been deveined and deboned, marinated with rhetoric, seasoned with narrative – now enter the oven and vote.

© 2007 IndiaUncut.com. All rights reserved.
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This is the 19th installment of The Rationalist, my column for the Times of India.

A friend of mine was very impressed by the interview Narendra Modi granted last week to Akshay Kumar. ‘Such a charming man, such great work ethic,’ he gushed. ‘He is the kind of uncle I would want my kids to have.’ And then, in the same breath, he asked, ‘How can such a good man be such a bad prime minister?”

I don’t want to be uncharitable and suggest that Modi’s image is entirely manufactured, so let’s take the interview at face value. Let’s also grant Modi his claims about the purity of his neeyat (intentions), and reframe the question this way: when it comes to public policy, why do good intentions often lead to bad outcomes? To attempt an answer, I’ll refer to a story a friend of mine, who knows Modi well, once told me about him. 

Modi was chilling with his friends at home more than a decade ago, and told them an incident from his childhood. His mother was ill once, and the young Narendra was tending to her. The heat was enervating, so the boy went to the switchboard to switch on the fan. But there was no electricity. My friend said that as he told this story, Modi’s eyes filled with tears. Even after all these years, he was moved by the memory.

My friend used this story to make the point that Modi’s vision of the world is experiential. If he experiences something, he understands it. When he became chief minister of Gujarat, he made it his stated mission to get reliable electricity to every part of Gujarat. No doubt this was shaped by the time he flicked a switch as a young boy and the fan did not budge. Similarly, he has given importance to things like roads and cleanliness, since he would have experienced the impact of those as a young man.

My term for him, inspired by Rajat Kapoor’s 2014 film, is ‘the ankhon dekhi prime minister’. At one level, this is a good thing. He sees a problem and works for the rest of his life to solve it. But what of things he cannot experience?

The economy is a complex beast, as is society itself, and beyond a certain level, you need to grasp abstract concepts to understand how the world works. You cannot experience them. For example, spontaneous order, or the idea that society and markets, like language, cannot be centrally directed or planned. Or the positive-sum nature of things, which is the engine of our prosperity: the idea that every transaction is a win-win game, and that for one person to win, another does not have to lose. Or, indeed, respect for individual rights and free speech.

One understands abstract concepts by reading about them, understanding them, applying them to the real world. Modi is not known to be a reader, and this is not his fault. Given his background, it is a near-miracle that he has made it this far. He wasn’t born into a home with a reading culture, and did not have either the resources or the time when he was young to devote to reading. The only way he could learn about the world, thus, was by experiencing it.

There are two lessons here, one for Modi himself and others in his position, and another for everyone.

The lesson in this for Modi is a lesson for anyone who rises to such an important position, even if he is the smartest person in the world. That lesson is to have humility about the bounds of your knowledge, and to surround yourself with experts who can advise you well. Be driven by values and not confidence in your own knowledge. Gather intellectual giants around you, and stand on their shoulders.

Modi did not do this in the case of demonetisation, which he carried out against the advice of every expert he consulted. We all know the damage it caused to the economy.

The other learning from this is for all of us. How do we make sense of the world? By connecting dots. An ankhon-dekhi approach will get us very few dots, and our view of the world will be blurred and incomplete. The best way to gather more dots is reading. The more we read, the better we understand the world, and the better the decisions we take. When we can experience a thousand lives through books, why restrict ourselves to one?

A good man with noble intentions can make bad decisions with horrible consequences. The only way to hedge against this is by staying humble and reading more. So when you finish reading this piece, think of an unread book that you’d like to read today – and read it!

© 2007 IndiaUncut.com. All rights reserved.
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This is the 20th installment of The Rationalist, my column for the Times of India.

Amit Shah’s induction into the union cabinet is such an interesting moment. Even partisans who oppose the BJP, as I do, would admit that Shah is a political genius. Under his leadership, the BJP has become an electoral behemoth in the most complicated political landscape in the world. The big question that now arises is this: can Shah do for India what he did for the BJP?

This raises a perplexing question: in the last five years, as the BJP has flourished, India has languished. And yet, the leadership of both the party and the nation are more or less the same. Then why hasn’t the ability to manage the party translated to governing the country?

I would argue that there are two reasons for this. One, the skills required in those two tasks are different. Two, so are the incentives in play.

Let’s look at the skills first. Managing a party like the BJP is, in some ways, like managing a large multinational company. Shah is a master at top-down planning and micro-management. How he went about winning the 2014 elections, described in detail in Prashant Jha’s book How the BJP Wins, should be a Harvard Business School case study. The book describes how he fixed the BJP’s ground game in Uttar Pradesh, picking teams for 147,000 booths in Uttar Pradesh, monitoring them, and keeping them accountable.

Shah looked at the market segmentation in UP, and hit upon his now famous “60% formula”. He realised he could not deliver the votes of Muslims, Yadavs and Jatavs, who were 40% of the population. So he focussed on wooing the other 60%, including non-Yadav OBCs and non-Jatav Dalits. He carried out versions of these caste reconfigurations across states, and according to Jha, covered “over 5 lakh kilometres” between 2014 and 2017, consolidating market share in every state in this country. He nurtured “a pool of a thousand new OBC and Dalit leaders”, going well beyond the posturing of other parties.

That so many Dalits and OBCs voted for the BJP in 2019 is astonishing. Shah went past Mandal politics, managing to subsume previously antagonistic castes and sub-castes into a broad Hindutva identity. And as the BJP increased its depth, it expanded its breadth as well. What it has done in West Bengal, wiping out the Left and weakening Mamata Banerjee, is jaw-dropping. With hindsight, it may one day seem inevitable, but only a madman could have conceived it, and only a genius could have executed it.

Good man to be Home Minister then, eh? Not quite. A country is not like a large company or even a political party. It is much too complex to be managed from the top down, and a control freak is bound to flounder. The approach needed is very different.

Some tasks of governance, it is true, are tailor-made for efficient managers. Building infrastructure, taking care of roads and power, building toilets (even without an underlying drainage system) and PR campaigns can all be executed by good managers. But the deeper tasks of making an economy flourish require a different approach. They need a light touch, not a heavy hand.

The 20th century is full of cautionary tales that show that economies cannot be centrally planned from the top down. Examples of that ‘fatal conceit’, to use my hero Friedrich Hayek’s term, include the Soviet Union, Mao’s China, and even the lady Modi most reminds me of, Indira Gandhi.

The task of the state, when it comes to the economy, is to administer a strong rule of law, and to make sure it is applied equally. No special favours to cronies or special interest groups. Just unleash the natural creativity of the people, and don’t try to micro-manage.

Sadly, the BJP’s impulse, like that of most governments of the past, is a statist one. India should have a small state that does a few things well. Instead, we have a large state that does many things badly, and acts as a parasite on its people.

As it happens, the few things that we should do well are all right up Shah’s managerial alley. For example, the rule of law is effectively absent in India today, especially for the poor. As Home Minister, Shah could fix this if he applied the same zeal to governing India as he did to growing the BJP. But will he?

And here we come to the question of incentives. What drives Amit Shah: maximising power, or serving the nation? What is good for the country will often coincide with what is good for the party – but not always. When they diverge, which path will Shah choose? So much rests on that.

© 2007 IndiaUncut.com. All rights reserved.
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First, the good news. The EDA industry will grow from $6.2 billion in 2015 to $9.0 billion in 2019, according to Gary Smith, chief analyst at Gary Smith EDA. Year-to-year growth rates will range from +4% to +11.2%.

But in his annual presentation on the eve of the Design Automation Conference (DAC 2015), Smith noted that Wall Street is unimpressed. “The people I talk to want long-term steady growth, no sharp up-turns, no sharp downturns,” Smith said. “To the rest of Wall Street, we’re boring.”

Smith spent the rest of his talk noting how EDA can be a lot less boring and, potentially, a whole lot bigger. For starters, what if we add semiconductor IP to EDA revenues? Now we’re looking at $12.2 billion in revenue by 2019, Smith said. (He acknowledged, however, that the IP market itself is going to take a “dip” due to the move towards platform-based IP and away from conventional piecemeal IP).

This still is not enough to get Wall Street’s attention. Another possibility is to bring embedded software development into the EDA industry. This is not a huge market – about $2.6 billion today – but it is an “easy growth market for us,” according to Smith.

Chasing the Big Bucks

But the “big bucks” are in mechanical CAD (MCAD), Smith said. In the past the MCAD market has always been bigger than EDA, but now EDA is catching up. The MCAD market is about $6.6 billion now. Synopsys and Cadence are larger than PTC and Siemens, two of the main players in MCAD.

There may be some good acquisition possibilities coming up for EDA vendors, Smith said – and if we don’t buy MCAD companies, they might buy EDA companies. Consider, for example, that Ansoft bought Apache and Dassault bought Synchronicity. (Note: Siemens PLM Software is a first-time exhibitor at DAC 2015).

What about other domains? Smith said that EDA companies could conceivably move into optical design, applications development software, biomedical design, and chemical design. The last if these is probably the most tenuous; Smith noted that EDA vendors have yet to look into chemical design.

Applications development software is the biggest market on the above list, but that means competing with Microsoft, IBM, and Oracle. “You’re in with the big boys – is that a good idea?” Smith asked.

Perhaps there’s an opening for a “big play” for an MCAD provider. Smith noted that mechanical vendors are focusing on product data management (PDM). This “is really the IT of design,” Smith said. “They have a lot of hope that the IoT [Internet of things] market is going to give them an opportunity to capture the software that goes from the ground to the cloud. Maybe we can let them have PDM and see if we can take the tool market away from them, or acquire it away from them.”

In conclusion, Smith asked, should the EDA industry accelerate its growth? “The mechanical vendors have already shown interest in acquiring EDA vendors,” he said. “We may not have a choice.”

Richard Goering

NOTE: Catch our live blog from DAC 2015, beginning Monday morning, June 8! Click here

There has been so much hype about the “Internet of Things” (IoT) that it is refreshing to hear about a cutting-edge development project that can bring concrete benefits to millions of people. That project is the ongoing development of the Google Smart Contact Lens, and it was detailed in a keynote speech June 8 at the Design Automation Conference (DAC 2015).

The keynote speech was given by Brian Otis (right), a director at Google and a research associate professor at the University of Washington. The “smart lens” that the project envisions is essentially a disposable contact lens that fits on an eye and continuously monitors blood glucose levels. This is valuable information for anyone who has, or may someday have, diabetes.

Since he was speaking to an engineering audience, Otis focused on the challenges behind building such a device, and described some of the strategies taken by Google and its partner, Novartis. The project required new approaches to miniaturization, low-power design, and connectivity, as well as a comfortable and reliable silicon-to-human interface. Otis discussed the “why” as well and showed how the device could potentially save or improve millions of lives.

Millions of Users

First, a bit of background. Google announced the smart lens project in a blog post in January 2014. Since then it has been featured in news outlets including Forbes, Time, and the Wall Street Journal. In March 2015, Time reported that Google has been granted a patent for a smart contact lens.

The smart lens monitors the level of blood glucose by looking at its concentration in tears. The lens includes a wireless system on chip (SoC) and a miniaturized glucose sensor. A tiny pinhole in the lens allows tear fluid to seep into the sensor, and a wireless antenna handles communications to the wireless devices.

“We figure that if we can solve a huge problem, it is probably worth doing,” Otis said. “Diabetes is one example.” He noted 382 million people worldwide have diabetes today, and that 35% of the U.S. population may be pre-diabetic. Today, diabetics must *** their fingers to test blood glucose levels, a procedure that is invasive, painful, and subject to infrequent monitoring.

According to Otis, the smart contact lens represents a “new category of wearable devices that are comfortable, inexpensive, and empowering.” The lens does sensor data logging and uses a portable instrument to measure glucose levels. It is thin, cheap, and disposable, he said.

Moreover, the lens is not just for people already diagnosed with diabetes—it’s for anyone who is pre-diabetic, or may be at risk due to genetic predisposition. “If we are pro-active rather than re-active,” Otis said, “Instead of waiting until a person has full-fledged diabetes, we could make a huge difference in peoples’ lives and lower the costs of treating them.”

Technical Challenges

No one has built anything quite like the smart lens, so researchers at Google and Novartis are treading new ground. Otis identified three key challenges:

• Miniaturization: Everything must be really small—the SoC, the passive components, the power supply. Components must be flexible and cheap, and support thin-film integration.
• Platform: Google has developed a reusable platform that includes tiny, always-on wireless sensors, ultra low-power components, and standards-based interfaces.
• Data: Researchers are looking for the best ways to get the resulting data into a mobile device and onto the cloud.

Comfort is another concern. “This is not intended to be for the most severe cases,” Otis said. “This is intended to be for all of us as a pro-active way of improving our lifestyles.”

The platform provides a bidirectional encrypted wireless link, integrated power management, on-chip memory, standards-based RFID link, flexible sensor interface, high-resolution potentiostat sensor, and decoupling capacitors. Most of these capabilities are provided by the standard CMOS SoC, which is a couple hundred microns on a side and only “tens of microns” thick.

Otis noted that unpackaged ICs are typically 250 microns thick when they come back from the foundry. Thus, post-processing is needed so the IC will fit into a contact lens.

Furthermore, the design requires precision analog circuitry and additional environmental sensors. “Some of this stuff sounds mundane but it is really hard, especially when you find out you can’t throw large decoupling capacitors and bypass capacitors onto a board, and all that has to be re-integrated into the chip,” Otis said.

Sensor Challenges

Getting information from the human body is challenging. The smart lens sensor does a direct chemical measurement on the surface of the eye. The sensor is designed to work with very low glucose concentrations. This is because the concentration of glucose in tears is an order of magnitude lower than it is in blood.

In brief, the sensor has two parallel plates that are coated with an enzyme that converts glucose into hydrogen peroxide, which flows around the electrodes of the sensor. This is actually a fairly standard way of doing glucose monitoring. However, the smart lens sensor has two electrodes compared to the typical three.

In manufacturing, it is essential to keep costs low. Otis outlined a three-step manufacturing process:

• Start with the bottom layer, and mold a contact lens in the way you typically would.
• Add the electronics package on top of that layer.
• Build a second layer that encapsulates the electronics and provides the curvature needed for comfort and vision correction.

Beyond the technical challenges are the “clinical” challenges of working with human beings. The human body “is messy and very variable,” Otis said. This variability affects sensor performance and calibration, RF/electro-magnetic performance, system reliability, and comfort.

The final step is making use of the data. “We need to get the data from the device into a phone, and then display it so users can visualize the data,” Otis said. This provides “actionable feedback” to the person who needs it. Eventually, the data will need to be stored in the cloud.

As he concluded his talk, Otis noted that the platform his group developed may have many applications beyond glucose monitoring. “There is a lot you can do with a bunch of logic and sensing capability,” he said, “and there are hundreds of biomarkers beyond glucose.” Clearly this will be an interesting technology to watch.

Richard Goering

Related Blog Post

- Gary Smith at DAC 2015: How EDA Can Expand Into New Directions

As a leading venture capitalist in the electronics technology, as well as CEO of Cadence, Lip-Bu Tan has unique insights into ongoing changes that will impact EDA providers and users. Tan shared some of those insights in a “fireside chat” with Ed Sperling, editor in chief of Semiconductor Engineering, at the Design Automation Conference (DAC 2015) on June 9.

Topics of this discussion included industry consolidation, the need for more talent and more startups, Internet of Things (IoT) opportunities and challenges, the shift from ICs to full product development, and the challenges of advanced nodes. Following are some excerpts from this conversation, held at the DAC Pavilion theater on the exhibit floor.

Ed Sperling (left) and Lip-Bu Tan (right) discuss trends in semiconductors and EDA

Q: As you look out over the semiconductor and EDA industries these days, what worries you most?

Tan: At the top of my list is all the consolidation that is going on. Secondly, chip design complexity is increasing substantially. Time-to-market pressure is growing and advanced nodes have challenges.

The other thing I worry about is that we need to have more startups. There’s a lot of innovation that needs to happen. And this industry needs more top talent. At Cadence, we have a program to recruit over 10% of new hires every year from college graduates. We need new blood and new ideas.

Q: EDA vendors were acquiring companies for many years, but now the startups are pretty much gone. Where does the next wave of innovation come from?

Tan: I’ve been an EDA CEO for the last seven years and I really enjoy it because so much innovation is needed. System providers have very big challenges and very different needs. You have to find the opportunities and go out and provide the solutions.

The opportunities are not just in basic tools. Massive parallelism is critical, and the power challenge is huge. Time to market is critical, and for the IoT companies, cost is going to be critical. If you want to take on some good engineering challenges, this is the most exciting time.

Q: You live two lives—you’re a CEO but you’re also an investor. Where are the investments going these days and where are we likely to see new startups?

Tan: Clearly everybody is chasing the IoT. There is a lot of opportunity in the cloud, in the data center. Also, I’m a big believer in video, so I back companies that are video related. A big area is automotive. ADAS [Advanced Driver Assistance Systems] is a tremendous opportunity.

These companies can help us understand how the industry is transforming, and then we can provide solutions, either in terms of IP, tools, or the PCB. Then we need to connect from the system level down to semiconductors. I think it’s a different way to design.

Q: What happens as we start moving from companies looking to design a semiconductor to system companies who are doing things from the perspective that we have this purpose for our software?

Tan: We are extending from EDA to what we call system design enablement, and we are becoming more application driven. The application at the system level will drive the silicon design. We need to help companies look at the whole system including the power envelope and signal integrity. You don’t want to be in a position where you design a chip all the way to fabrication and then find the power is too high.

We help the customers with hardware/software co-design and co-verification. We have a design suite and a verification suite that can provide customers with high-level abstractions, as well as verify IP blocks at the system level. Then we can break things down to the component level with system constraints in mind, and drive power-aware, system-aware design.

We are starting to move into vertical markets. For example, medical is a tremendous opportunity.

Q: How does this approach change what you provide to customers?

Tan: Every year I spend time meeting with customers. I think it is very important to understand what they are trying to design, and it is also important to know the customer’s customer requirements. We might say, “Wait a minute, for this design you may want to think about power or the library you’re using.” We help them understand what foundry they should use and what process they should use. They don’t view me as a vendor—they view me as a partner.

We also work very closely with our IP and foundry partners. We work as one team—the ultimate goal is customer success.

Q: Is everybody going to say, FinFETs are beautiful, we’re going to go down to 10nm or 7nm—or is it a smaller number of companies who will continue down that path?

Tan: Some of the analog/mixed-signal companies don’t need to go that far. We love those customers—we have close to 50% of that business. But we also have customers in the graphics or processor area who are really pushing the envelope, and need to be in 16nm, 14nm, or 10nm. We work very closely with those guys to make sure they can go into FinFETs.

We always want to work with the customer to make sure they have a first-time silicon success. If you have to do a re-spin, you miss the opportunity and it’s very costly.

Q: There’s a new market that is starting to explode—IoT. How real is that world to you? Everyone talks about large numbers, but is it showing up in terms of tools?

Tan: Everybody is talking about huge profits, but a lot of the time I think it is just connecting old devices that you have. Billions of units, absolutely yes, but if you look close enough the silicon percentage of that revenue is very tiny. A lot of the profit is on the service side. So you really need to look at the service killer app you are trying to provide.

What’s most important to us in the IoT market is the IP business. That’s why we bought Tensilica—it’s programmable, so you can find the killer app more quickly. The other challenges are time to market, low power, and low cost.

Q: Where is system design enablement going? Does it expand outside the traditional market for EDA?

Tan: It’s not just about tools. IP is now 11% of our revenue. At the PCB level, we acquired a company called Sigrity, and through that we are able to drive system analysis for power, signal integrity, and thermal. And then we look at some of the verticals and provide modeling all the way from the system level to the component level. We make sure that we provide a solution to the end customer, rather than something piecemeal.

Q: What do you think DAC will look like in five years?

Tan: It’s getting smaller. We need to see more startups and innovative IP solutions. I saw a few here this year, and that’s good. We need to encourage small startups.

Q: Where do we get the people to pull this off? I don’t see too many people coming into EDA.

Tan: I talk to a lot of university students, and I tell them that this small industry is a gold mine. A lot of innovation is needed. We need them to come in [to EDA] rather than join Google or Facebook. Those are great companies, but there is a lot of fundamental physical innovation we need.

Richard Goering

Related Blog Posts

- Gary Smith at DAC 2015: How EDA Can Expand Into New Directions

- DAC 2015: Google Smart Contact Lens Project Stretches Limits of IC Design

- Q&A with Nimish Modi: Going Beyond Traditional EDA

Let’s face it – the EDA industry needs new people and new ideas. One of the best places to find both is academia, and a presentation at the Cadence Theater at the recent Design Automation Conference (DAC 2015) described collaboration models that are working today.

The presentation was titled “Industry/Academia Engagement Models – From PhD Contests to R&D Collaborations.” It included these speakers, shown from left to right in the photo below:

• Prof. Xin Li, Electrical and Computer Engineering, Carnegie-Mellon University (CMU)
• Chuck Alpert, Senior Software Architect, Cadence
• Prof. Laleh Behjat, Department of Electrical and Computer Engineering, University of Calgary

Alpert, who was filling in for Zhuo Li, Software Architect at Cadence, was the vice chair of DAC 2015 and will be the general chair of DAC 2016 in Austin, Texas. “My team at Cadence really likes to collaborate with universities,” he said. “We’re a big proponent of education because we really need the best and brightest students in our industry.”

Contests Boost EDA Research

One way that Cadence collaborates with academia is participation in contests. “It’s a great way to formulate problems to academia,” Alpert said. “We can have the universities work on these problems and get some strategic direction.”

For example, Cadence has been involved with the annual CAD contest at the International Conference on Computer-Aided Design (ICCAD) since the contest was launched in 2012. This is the largest worldwide EDA R&D contest, and it is sponsored by the IEEE Council on EDA (CEDA) and the Taiwan Ministry of Education. Its goals are to boost EDA research in advanced real-world problems and to foster industry-academia collaboration.

Contestants can participate in one of more problems in the three areas of system design, logic synthesis and verification, and physical design. The 2015 contest has attracted 112 teams from 12 regions. Cadence contributes one problem per year in the logic synthesis area. Zhuo Li was the 2012 co-chair and the 2013 chair. The awards will be given at ICCAD in November 2015.

Another step that Cadence has taken, Alpert said, is to “hire lots of interns.” His own team has four interns at the moment. One advantage to interning at Cadence, he said, is that students get to see real-world designs and understand how the tools work. “It helps you drive your research in a more practical and useful direction,” he said.

The Cadence Academic Network co-sponsors the ACM SIGDA PhD Forum at DAC, and Xin Li and Zhuo Li are on the organizing committee. This event is a poster session for PhD students to present and discuss their dissertation research with people in the EDA community. This year’s forum was “packed,” Alpert said, and it’s clear that the event needs a bigger room.

Finally, Alpert noted, Cadence researchers write and publish technical papers at DAC and other conferences, and Cadence people serve on the DAC technical program committee. “We try to be involved with the academic community on a regular basis,” Alpert said. “We want the best and the brightest people to go into EDA because there is still so much innovation that’s needed. It’s a really cool place to be.”

Research Collaboration Exposes Failure Rates

Xin Li presented an example of a successful research collaboration between CMU and Cadence. The challenge was to find a better way to estimate potential failure rates in memory. As noted in a previous blog post, PhD student Shupeng Sun met this challenge with a new statistical methodology that won a Best Poster award at the ACM SIGDA PhD Forum at DAC 2014.

The new methodology is called Scaled-Sigma Sampling (SSS). It calculates the failure rate and accounts for variability in the manufacturing process while only requiring a few hundred, or a few thousand, sample circuit blocks. Previously, millions of samples were required for an accurate validation of a new design, and each sample could take minutes or hours to simulate. It could take a few weeks or months to run one validation.

The SSS methodology requires greatly reduced simulation times. It makes it possible, Li noted, to run simulations overnight and see the results in the morning.

Li shared his secret for success in collaborations. “I want to emphasize that before the collaboration, you have to understand the goal. If you don’t have a clear goal, don’t collaborate. Once you define the goal, stick to it and make it happen.”

Contest Provides Learning Experience

Last year Laleh Behjat handed two of her new PhD students a challenge. “I told them there is an ISPD [International Symposium for Physical Design] contest on placement, and I expect you to participate and I expect you to win. Not knowing anything about placement, I don’t think they realized what I was asking them.”

The 2015 contest was called the Blockage-Aware Detailed Routing-Driven Placement Contest. Results were announced at the end of March at ISPD. And the University of Calgary team, despite its lack of placement experience, took second place.

Such contests provide a good learning tool, according to Behjat. Graduate students in EDA, she said, “have to be good programmers. They have to work in teams and be collaborative, be able to innovate, and solve the hardest problems I have seen in engineering and science. And they have to think outside the box.” A contest can bring out all these attributes, she said.

Further, Behjat noted, contest participants had access to benchmarks and to a placement tool. They didn’t have to write tools to find out if their results were good. Industry sponsors, meanwhile, got access to good students and new approaches for solving problems.

“You can see Cadence putting a big amount of time, effort and money to get students here and get them excited about doing contests,” she said. She advised students in the theater audience to “talk to people in the Cadence booth and see if you can have more ideas for collaboration.”

Richard Goering

Related Blog Posts

EDA Plus Academia: A Perfect Game, Set and Match

Cadence Aims to Strengthen Academic Partnerships

BSIM-CMG FinFET Model – How Academia and Industry Empowered the Next Transistor

EDA investor and former executive Jim Hogan is optimistic about automotive electronics, but he has some concerns as well. At the recent Design Automation Conference (DAC 2015), he delivered a speech titled “The Looming Quality, Reliability, and Safety Crisis in Automotive Electronics...Why is it and what can we do to avoid it?"

Hogan gave the keynote speech for IP Talks!, a series of over 30 half-hour presentations located at the ChipEstimate.com booth. Presenters included ARM, Cadence, eSilicon, Kilopass, Sidense, SilabTech, Sonics, Synopsys, True Circuits, and TSMC. Held in an informal setting, the talks addressed the challenges faced by SoC design teams and showed how the latest developments in semiconductor IP can contribute to design success.

Jim Hogan delivers keynote speech at DAC 2015 IP Talks!

Hogan talked about several phases of automotive electronics. These include assisted driving to avoid collisions, controlled automation of isolated tasks such as parallel parking, and, finally, fully autonomous vehicles, which Hogan expects to see in 15 to 20 years. The top immediate priorities for automotive electronics designers, he said, will be government regulation, fuel economy, advanced safety, and infotainment.

More Code than a Boeing 777

According to Hogan, today’s automobiles use 50-100 microcontrollers per car, resulting in a worldwide automotive semiconductor market of around $40 billion. The global market for advanced automotive electronics is expected to reach $240 billion by 2020. Software is growing faster in the automotive market than it is in smartphones. Hogan quoted a Ford vice president who observed that there are more lines of code in a Ford Fusion car than a Boeing 777 airplane.

One unique challenge for automotive electronics designers is long-term reliability. This is because a typical U.S. car stays on the road for 15 years, Hogan said. Americans are holding onto new vehicles for a record 71.4 months.

Another challenge is regulatory compliance. Aeronautics is highly regulated from manufacturing to air traffic control, and the same will probably be true of automated cars. Hogan speculated that the Department of Transportation will be the regulatory authority for autonomous cars. Today, automotive electronics providers must comply with the ISO26262 automotive functional safety specification.

So where do we go from here? “We’ve got to change our mindset,” Hogan said. “We’ve got to focus on safety and reliability and demand a different kind of engineering discipline.” You can watch Hogan’s entire presentation by clicking on the video icon below, or clicking here. You can also watch other IP Talks! videos from DAC 2015 here.

https://youtu.be/qL4kAEu-PNw

Richard Goering

Related Blog Posts

DAC 2015: See the Latest in Semiconductor IP at “IP Talks!”

Automotive Functional Safety Drives New Chapter in IC Verification

Dear all,

I am using Conformal Constraint Designer (Version 17.1) to analyse a SystemVerilog based design.

While performing default HDL checks it finds  some violations (issues) in RTL and complains (warnings, etc) about RTL checks and others.

My questions:

Is there any directive which I can add to RTL (system Verilog) so that particular line of code or signal is ignored or not checked for HDL or RTL checks.

I can set ignore rules in rule manager (gui) but it does not seems effective if code line number changes or new signals are introduced.

What is the best way to customize default_hdl_rules ?

I will be grateful for your guidance.

Thanks for your time.

Happy New Year! Have you tried the new Transmission Line Library (rfTlineLib) yet? In case you missed it, rfTlineLib was introduced in IC 6.1.6 ISR1 plus MMSIM 12.1.1 -or- MMSIM13.1. You may wonder....Why should I use the new rfTlineLib ? Well...(read more)

Hi Folks, A question that I've often received from designers, "Is there a method to determine the amount of memory required before I submit a job? I use distributed processing and need to provide an estimate before submitting jobs." The answer...(read more)

RF Enthusiasts, Come connect with Cadence RF experts and discover the latest advances in Cadence RF technologies, including Spectre RF at the IEEE International Microwave Symposium (IMS) 2014. This year, IMS will be held in Tampa, Florida. Cadence...(read more)

Hello Spectre RF Users, Next week is my all time favorite technical conference - the International Microwave Symposium IMS2016 , May 22-27 in San Francisco, CA at the Moscone Center. If you're at the conference, please stop by the Cadence booth and...(read more)

Hi,

I would like to add items like length of selected metal or area also in status tool bar. I have tried below option but I am getting warning as shown below. Could you please give suggestions. 

envGetVal("layout" "statusToolbarFields")

*WARNING* envGetVal: Could not find variable 'statusToolbarFields' in tool[.partition] 'layout'

Regards,

Varsha

As the stakes for winning server segment market share grow ever higher an increasing number of companies are seeking to grasp the latest Holy Grail of multi-chip coherence. The approach promises to better enable applications such as machine learning...(read more)

It’s been quite a long 5-year journey building and deploying Performance Analysis, Verification, and Debug capabilities for Arm-based SoCs. We worked with some of the smartest engineers on the planet. First with the engineers at Arm, with whom we...(read more)

There’s nothing like the heat of a DAC demo to stress new technology and the engineers behind it! Such was the case at DAC 2018 at the new locale of Moscone Center West, San Francisco. Cadence and AMIQ were two of several vendors who announced ...(read more)

A little bit of everything in the blog today: PSS is All Over As someone that was involved with UVM and PSS, both becoming Accellera standards, it is exciting to see both growing independently and together. With PSS we had a massive amount of papers ...(read more)

I recently attended a webinar presented by Wally Rhines about his new book, Predicting Semiconductor Business Trends After Moore's Law . Wally was the CEO of Mentor, as you probably know. Now he...

[[ Click on the title to access the full blog on the Cadence Community site. ]]

Everyone keeps talking about “the cloud” this and “the cloud” that these days—but you’re a semiconductor designer. Everyone keeps saying “the cloud” is revolutionizing all aspects of electronics design—but what does it mean for you? Cadence's own Tom Hackett discussed this in a presentation at the Cadence Theater during DAC 2019.

What people refer to as “the cloud” is commonly divided into three categories: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and software as a Service (SaaS). With IaaS, you bring your own software—i.e. loading your owned or appropriately licensed tools onto cloud hardware that you rent by the minute. This service is available from providers like Google Cloud Platform, Amazon Web Service, and Microsoft Azure. In PaaS (also available from the major cloud providers), you create your own offering using capabilities and a software design environment provided by the cloud vendor that makes subsequent scaling and distribution really easy because the service was “born in the cloud”.  Lastly, there’s SaaS, where the cloud is used to access and manage functionality and data without requiring users to set up or manage any of the underlying infrastructure used to provide it.  SaaS companies like Workday and Salesforce deliver their value in this manner.  The Cadence Cloud portfolio makes use of both IaaS and SaaS, depending on the customers’ interest.  Cadence doesn’t have PaaS offerings because our customers don’t create their own EDA software from building blocks that Cadence provides.

All of these designations are great, but you’re a semiconductor designer. Presumably you use Workday or some similar software, or have in the past when you were an intern, but what about all of your tools? Those aren’t on the cloud.

Wait—actually, they are.

Using EDA tools in the cloud allows you to address complexity and data explosion issues you would have to simply struggle through before. Since you don’t have to worry about having the compute-power on-site, you can use way more power than you could before. You may be wary about this new generation of cloud-based tools, but don’t worry: the old rules of cloud computing no longer apply. Cloud capacity is far larger than it used to be, and it’s more secure. Updates to scheduling software means that resource competition isn’t as big of a deal anymore. Clouds today have nearly unlimited capacity—they’re so large that you don’t ever need to worry about running out of space.

The vast increase in raw compute available to designers through the cloud makes something like automotive functional safety verification, previously an extremely long verification task, doable in a reasonable time frame. With the cloud, it’s easy to scale the amount of compute you’re using to fit your task—whether it’s an automotive functional safety-related design or a small one.

Nowadays, the Cadence Cloud Portfolio brings you the best and brightest in cloud technology. No matter what your use case is, the Cadence Cloud Portfolio has a solution that works for you. You can even access the Palladium Cloud, allowing you to try out the benefits of an accelerator without having to buy one.

Cloud computing is the future of EDA. See the future here.

All engineers can enhance their mixed-signal low-power structural verification productivity by learning while doing with a PIEA RAK (Power Intent Export Assistant Rapid Adoption Kit). They can verify the mixed-signal chip by a generating macromodel for their analog block automatically, and run it through Conformal Low Power (CLP) to perform a low power structural check.  

The power structure integrity of a mixed-signal, low-power block is verified via Conformal Low Power integrated into the Virtuoso Schematic Editor Power Intent Export Assistant (VSE-PIEA). Here is the flow.

Applying the flow iteratively from lower to higher levels can verify the power structure.

Cadence customers can learn more in a Rapid Adoption Kit (RAK) titled IC 6.1.5 Virtuoso Schematic Editor XL PIEA, Conformal Low Power: Mixed-Signal Low Power Structural Verification.

• To read the overview presentation, click on following link: PIEA Overview
• To download this PIEA RAK click on following link: PIEA RAK Download

The RAK includes Rapid Adoption Kit with demo design (instructions are provided on how to setup the user environment). It Introduces the Power Intent Export Assistant (PIEA) feature that has been implemented in the Virtuoso IC615 release.  The power intent extracted is then verified by calling Conformal Low Power (CLP) inside the Virtuoso environment.

• Last Update: 11/15/2012.
• Validated with IC 6.1.5 and CLP 11.1

The RAK uses a sample test case to go through PIEA + CLP flow as follows:

• Setup for PIEA
• Perform power intent extraction
• CPF Import: It is recommended to Import macro CPF, as oppose to designing CPF for sub-blocks. If you choose to import design CPF files please make sure the design CPF file has power domain information for all the top level boundary ports
• Generate macro CPF and design CPF
• Perform low power verification by running CLP

It is also recommended to go through older RAKs as prerequisites.

• Conformal Low Power, RTL Compiler and Incisive: Low Power Verification for Beginners
• Conformal Low Power: CPF Macro Models
• Conformal Low Power and RTL Compiler: Low Power Verification for Advanced Users

To access all these RAKs, visit our RAK Home Page to access Synthesis, Test and Verification flow

Note: To access above docs, use your Cadence credentials to logon to the Cadence Online Support (COS) web site. Cadence Online Support website https://support.cadence.com/ is your 24/7 partner for getting help and resolving issues related to Cadence software. If you are signed up for e-mail notifications, you can receive new solutions, Application Notes (Technical Papers), Videos, Manuals, and more.

You can send us your feedback by adding a comment below or using the feedback box on Cadence Online Support.

Sumeet Aggarwal

DAC is right around the corner! On the demo floor at Cadence® Booth #2214, we will demonstrate how to use the Cadence mixed-signal and low-power solution to design, verify, and implement a microcontroller-based mixed-signal design. The demo design architecture is very similar to practical designs of many applications like power management ICs, automotive controllers, and the Internet of Things (IoT). Cadene tools demonstrated in this design include Virtuoso® Schematic Editor, Virtuoso Analog Design Environment, Virtuoso AMS Designer, Virtuoso Schematic Model Generator, Virtuoso Power Intent Assistant, Incisive® Enterprise Simulator with DMS option, Virtuoso Digital Implementation, Virtuoso Layout Suite, Encounter® RTL Compiler, Encounter Test, and Conformal Low Power. An extended version of this demo will also be shown at the ARM® Connected Community Pavilion Booth #921.

For additional highlights on Cadence mixed-signal and low-power solutions, stop by our booth for:

• The popular book, Mixed-signal Methodology Guide, which will be on sale during DAC week!
• A sneak preview of the eBook version of the Mixed-signal Methodology Guide
• Customer presentations at the Cadence DAC Theater
  • 9am, Tuesday, June 4  ARM  Low-Power Verification of A15 Hard Macro Using CLP 
  • 10:30am, Tuesday, June 4  Silicon Labs  Power Mode Verification in Mixed-Signal Chip
  • 12:00pm, Tuesday, June 4  IBM  An Interoperable Flow with Unified OA and QRC Technology Files
  • 9am, Wednesday, June 5  Marvell  Low-Power Verification Using CLP
  • 4pm, Wednesday, June 5  Texas Instruments  An Inter-Operable Flow with Unified OA and QRC Technology Files
• Partner presentations at the Cadence DAC Theater
  • 10am, Monday, June 3  X-Fab  Rapid Adoption of Advanced Cadence Design Flows Using X-FAB's AMS Reference Kit
  • 3:30pm, Monday, June 3  TSMC TSMC Custom Reference Flow for 20nm -  Cadence Track
  • 9:30am,Tuesday, June 4  TowerJazz   Substrate Noise Isolation Extraction/Model Using Cadence Analog Flow
  • 12:30pm, Wednesday, June 5  GLOBALFOUNDRIES  20nm/14nm Analog/Mixed-signal Flow
  • 2:30pm, Wednesday, June 5  ARM  Cortex®-M0 and Cortex-M0+: Tiny, Easy, and Energy-efficient Processors for Mixed-signal Applications
• Technology sessions at suites
  • 10am, Monday, June 3    Low-power Verification of Mixed-signal Designs
  • 2pm, Monday, June 3      Advanced Implementation Techniques for Mixed-signal Designs
  • 2pm, Monday, June 3      LP Simulation: Are You Really Done?
  • 4pm, Monday, June 3      Power Format Update: Latest on CPF and IEEE 1801  
  • 11am, Wednesday, June 5   Mixed-signal Verification
  • 11am, Wednesday, June 5   LP Simulation: Are You Really Done?
  • 4pm, Wednesday, June 5   Successful RTL-to-GDSII Low-Power Design (FULL)
  • 5pm, Wednesday, June 5   Custom/AMS Design at Advanced Nodes

We will also have three presentations at the Si2 booth (#1427):

• 10:30am, Monday, June 3   An Interoperable Implementation Solution for Mixed-signal Design
• 11:30am, Tuesday, June 4   Low-power Verification for Mixed-signal Designs Using CPF
• 10:30am, Wednesday, June 5   System-level Low-power Verification Using Palladium

We have a great program at DAC. Click the link for complete Cadence DAC Theater and Technology Sessions. Look forward to seeing you at DAC!     

ST Microelectronics reported their success with IEEE 1801 / UPF low-power simulation using Incisive Enterprise Simulator at CDNLive India in November 2013. We were able to meet with Mohit Jain just after his presentation and recorded this video that explains the key points in his paper.

With eight years of experience and pioneering technology in native low-power simulation, Mohit was able to apply Incisive Enterprise Simulator to a low-power demonstrator in preparation for use with a production set-top box chip.  Mohit was impressed with the ease in which he was able to reuse his existing IEEE 1801 / UPF code successfully, including the power format files and the macro models coded in his Liberty files. Mohit also discusses how he used the power-aware Cadence SimVision debugger.

The Cadence low-power verification solution for IEEE 1801 / UPF also incorporates the patent-pending Power Supply Network visualization in the SimVision debugger.  You can learn more about that in the Incisive low-power verification Rapid Adoption Kit for IEEE 1801 / UPF here in Cadence Online Support.

Just another happy Cadence low-power verification user!

Regards,

 Adam "The Jouler" Sherer 

Hello

The document I referenced is https://filebox.ece.vt.edu/~symort/rfworkshop/Mixer_workshop_instruction.pdf. (This is cadence workshop document)

While following the pxf simulation in the above article, the results are different and I have a question.

My result picture is shown below.

<my result error>

<document result>

<my direct plot>

<document direct plot>

The difference with the documentation is that in the direct plot screen after the pxf simulation,

1.output harmonics-> input sideband

2.Frequency axis: out-> frequency axis: absin

3.The results for port0 (RF port) are also different (see photo below).

4.The frequency values in the box are different.

My screen shows 5G, 10G, 1K ~ 10M, but the document is the same as 1K ~ 10M.

Ask for a solution. Thank you.

Hi all,

What is the most accurate way to simulate the gm of  RF transistors (RF stage) of an active mixer (single balanced or Gilbert cell)?

I tried to simulate it with many ways such as:

1. DC annotation (but of course its incorrect due to the switching operation of the mixer)

2. d(i_ds)/d(v_gs) using HB analysis and then taking the value at zero (since it is a DC characteristic). In this way I chose in the simulator results of HB: Voltage, spectrum, rms, magnitude. 

3. Using the OP, OPT buttons in the calculator and then extracting the gm of the transistor. 

The problem is that each way gives a different value which makes the procedure of designing an active mixer very difficult. In addition, when I simulate the voltage conversion gain of the active mixer and trying to compare it to the formula (2/pi)*gm*RL (either in linear or dB), I get numbers which are way too far from simulations. I understand that I would not get the same results but not different by hundreds percent. 

I see in many publications that people are plotting graphs of mixer's gm vs. different parameters and starting to doubt whether the results are correct.

I would appreciate any help,

Thanks in advance

Hello, I looking for reaching minimal NOISE FIRUGE by peaking a combination of the RLC on the output load as shown bellow.
I tried to do it with PNOISE  but it gives me an error on the |f(in)| i am not sure regarding the sidebands in my case(its not a mixer)

How can i show Noise Figure and optimize it using my output RLC?

Thanks

Hi all,

I am having trouble plotting the IIp3 of gilber RF mixer I made

I have plotted 1 dB compression point using QPSS and QPAC simulation. flo=2.42GHz and frf=2.4GHz , 20 MHz IF

However my IIp3 simulation shows strange results

QPSS and QPAC setup

Currently I tried this:

axlDBCreateShape(recPolyPlanes t "BOUNDARY/L02" netName sym1)

I get a atom error on car(sym1)

I can do this "static" using ETCH/L02 with out an issue, but I am trying to avoid doing an axlShapeChangeDynamicType().

Thanks,

Jerry

Hello, i am a student who is studying Allegro tool with SKILL.

I have a question about SKILL axlSegDelayAndZ0. The reference says this function "returns the delay and impedance of a cline segment."

I want to know how many components does this tool consider when calculating timing delay from the length. 

How steep is input signal's rise transition? Is rise transition shape isosceles trapezoid or differential increasing shape?

Also, if it is a multi fan-out, the rise transition time will be different net by net. How can this tool can calculate in this case?

I want to hear answers about these questions.

Thank you for reading this long boring questions, and i will be waiting for answers.

Hi, I'm trying to create a circle from a pin with 10 mil air gap and at 45 degree rotation. The problem that im facing is that, I'm unable to get the bBox upper left coordinates. Because I want my circle to be placed from that coordinate with a 10 mil air gap. And the pins are "regular" and are placed on "Etch/Top" Layer. Kindly help me in solving this issue.

This is the 15th installment of The Rationalist, my column for the Times of India.

It’s election season, and promises are raining down on voters like rose petals on naïve newlyweds. Earlier this week, the Congress party announced a minimum income guarantee for the poor. This Friday, the Modi government released a budget full of sops. As the days go by, the promises will get bolder, and you might feel important that so much attention is being given to you. Well, the joke is on you.

Every election, HL Mencken once said, is “an advance auction sale of stolen goods.” A bunch of competing mafias fight to rule over you for the next five years. You decide who wins, on the basis of who can bribe you better with your own money. This is an absurd situation, which I tried to express in a limerick I wrote for this page a couple of years ago:

POLITICS: A neta who loves currency notes/ Told me what his line of work denotes./ ‘It is kind of funny./ We steal people’s money/And use some of it to buy their votes.’

We’re the dupes here, and we pay far more to keep this circus going than this circus costs. It would be okay if the parties, once they came to power, provided good governance. But voters have given up on that, and now only want patronage and handouts. That leads to one of the biggest problems in Indian politics: We are stuck in an equilibrium where all good politics is bad economics, and vice versa.

For example, the minimum guarantee for the poor is good politics, because the optics are great. It’s basically Garibi Hatao: that slogan made Indira Gandhi a political juggernaut in the 1970s, at the same time that she unleashed a series of economic policies that kept millions of people in garibi for decades longer than they should have been.

This time, the Congress has released no details, and keeping it vague makes sense because I find it hard to see how it can make economic sense. Depending on how they define ‘poor’, how much income they offer and what the cost is, the plan will either be ineffective or unworkable.

The Modi government’s interim budget announced a handout for poor farmers that seemed rather pointless. Given our agricultural distress, offering a poor farmer 500 bucks a month seems almost like mockery.

Such condescending handouts solve nothing. The poor want jobs and opportunities. Those come with growth, which requires structural reforms. Structural reforms don’t sound sexy as election promises. Handouts do.

A classic example is farm loan waivers. We have reached a stage in our politics where every party has to promise them to assuage farmers, who are a strong vote bank everywhere. You can’t blame farmers for wanting them – they are a necessary anaesthetic. But no government has yet made a serious attempt at tackling the root causes of our agricultural crisis.

Why is it that Good Politics in India is always Bad Economics? Let me put forth some possible reasons. One, voters tend to think in zero-sum ways, as if the pie is fixed, and the only way to bring people out of poverty is to redistribute. The truth is that trade is a positive-sum game, and nations can only be lifted out of poverty when the whole pie grows. But this is unintuitive.

Two, Indian politics revolves around identity and patronage. The spoils of power are limited – that is indeed a zero-sum game – so you’re likely to vote for whoever can look after the interests of your in-group rather than care about the economy as a whole.

Three, voters tend to stay uninformed for good reasons, because of what Public Choice economists call Rational Ignorance. A single vote is unlikely to make a difference in an election, so why put in the effort to understand the nuances of economics and governance? Just ask, what is in it for me, and go with whatever seems to be the best answer.

Four, Politicians have a short-term horizon, geared towards winning the next election. A good policy that may take years to play out is unattractive. A policy that will win them votes in the short term is preferable.

Sadly, no Indian party has shown a willingness to aim for the long term. The Congress has produced new Gandhis, but not new ideas. And while the BJP did make some solid promises in 2014, they did not walk that talk, and have proved to be, as Arun Shourie once called them, UPA + Cow. Even the Congress is adopting the cow, in fact, so maybe the BJP will add Temple to that mix?

Benjamin Franklin once said, “Democracy is two wolves and a lamb voting on what to have for lunch.” This election season, my friends, the people of India are on the menu. You have been deveined and deboned, marinated with rhetoric, seasoned with narrative – now enter the oven and vote.

The India Uncut Blog © 2010 Amit Varma. All rights reserved.
Follow me on Twitter.

This is the 19th installment of The Rationalist, my column for the Times of India.

A friend of mine was very impressed by the interview Narendra Modi granted last week to Akshay Kumar. ‘Such a charming man, such great work ethic,’ he gushed. ‘He is the kind of uncle I would want my kids to have.’ And then, in the same breath, he asked, ‘How can such a good man be such a bad prime minister?”

I don’t want to be uncharitable and suggest that Modi’s image is entirely manufactured, so let’s take the interview at face value. Let’s also grant Modi his claims about the purity of his neeyat (intentions), and reframe the question this way: when it comes to public policy, why do good intentions often lead to bad outcomes? To attempt an answer, I’ll refer to a story a friend of mine, who knows Modi well, once told me about him. 

Modi was chilling with his friends at home more than a decade ago, and told them an incident from his childhood. His mother was ill once, and the young Narendra was tending to her. The heat was enervating, so the boy went to the switchboard to switch on the fan. But there was no electricity. My friend said that as he told this story, Modi’s eyes filled with tears. Even after all these years, he was moved by the memory.

My friend used this story to make the point that Modi’s vision of the world is experiential. If he experiences something, he understands it. When he became chief minister of Gujarat, he made it his stated mission to get reliable electricity to every part of Gujarat. No doubt this was shaped by the time he flicked a switch as a young boy and the fan did not budge. Similarly, he has given importance to things like roads and cleanliness, since he would have experienced the impact of those as a young man.

My term for him, inspired by Rajat Kapoor’s 2014 film, is ‘the ankhon dekhi prime minister’. At one level, this is a good thing. He sees a problem and works for the rest of his life to solve it. But what of things he cannot experience?

The economy is a complex beast, as is society itself, and beyond a certain level, you need to grasp abstract concepts to understand how the world works. You cannot experience them. For example, spontaneous order, or the idea that society and markets, like language, cannot be centrally directed or planned. Or the positive-sum nature of things, which is the engine of our prosperity: the idea that every transaction is a win-win game, and that for one person to win, another does not have to lose. Or, indeed, respect for individual rights and free speech.

One understands abstract concepts by reading about them, understanding them, applying them to the real world. Modi is not known to be a reader, and this is not his fault. Given his background, it is a near-miracle that he has made it this far. He wasn’t born into a home with a reading culture, and did not have either the resources or the time when he was young to devote to reading. The only way he could learn about the world, thus, was by experiencing it.

There are two lessons here, one for Modi himself and others in his position, and another for everyone.

The lesson in this for Modi is a lesson for anyone who rises to such an important position, even if he is the smartest person in the world. That lesson is to have humility about the bounds of your knowledge, and to surround yourself with experts who can advise you well. Be driven by values and not confidence in your own knowledge. Gather intellectual giants around you, and stand on their shoulders.

Modi did not do this in the case of demonetisation, which he carried out against the advice of every expert he consulted. We all know the damage it caused to the economy.

The other learning from this is for all of us. How do we make sense of the world? By connecting dots. An ankhon-dekhi approach will get us very few dots, and our view of the world will be blurred and incomplete. The best way to gather more dots is reading. The more we read, the better we understand the world, and the better the decisions we take. When we can experience a thousand lives through books, why restrict ourselves to one?

A good man with noble intentions can make bad decisions with horrible consequences. The only way to hedge against this is by staying humble and reading more. So when you finish reading this piece, think of an unread book that you’d like to read today – and read it!

The India Uncut Blog © 2010 Amit Varma. All rights reserved.
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This is the 20th installment of The Rationalist, my column for the Times of India.

Amit Shah’s induction into the union cabinet is such an interesting moment. Even partisans who oppose the BJP, as I do, would admit that Shah is a political genius. Under his leadership, the BJP has become an electoral behemoth in the most complicated political landscape in the world. The big question that now arises is this: can Shah do for India what he did for the BJP?

This raises a perplexing question: in the last five years, as the BJP has flourished, India has languished. And yet, the leadership of both the party and the nation are more or less the same. Then why hasn’t the ability to manage the party translated to governing the country?

I would argue that there are two reasons for this. One, the skills required in those two tasks are different. Two, so are the incentives in play.

Let’s look at the skills first. Managing a party like the BJP is, in some ways, like managing a large multinational company. Shah is a master at top-down planning and micro-management. How he went about winning the 2014 elections, described in detail in Prashant Jha’s book How the BJP Wins, should be a Harvard Business School case study. The book describes how he fixed the BJP’s ground game in Uttar Pradesh, picking teams for 147,000 booths in Uttar Pradesh, monitoring them, and keeping them accountable.

Shah looked at the market segmentation in UP, and hit upon his now famous “60% formula”. He realised he could not deliver the votes of Muslims, Yadavs and Jatavs, who were 40% of the population. So he focussed on wooing the other 60%, including non-Yadav OBCs and non-Jatav Dalits. He carried out versions of these caste reconfigurations across states, and according to Jha, covered “over 5 lakh kilometres” between 2014 and 2017, consolidating market share in every state in this country. He nurtured “a pool of a thousand new OBC and Dalit leaders”, going well beyond the posturing of other parties.

That so many Dalits and OBCs voted for the BJP in 2019 is astonishing. Shah went past Mandal politics, managing to subsume previously antagonistic castes and sub-castes into a broad Hindutva identity. And as the BJP increased its depth, it expanded its breadth as well. What it has done in West Bengal, wiping out the Left and weakening Mamata Banerjee, is jaw-dropping. With hindsight, it may one day seem inevitable, but only a madman could have conceived it, and only a genius could have executed it.

Good man to be Home Minister then, eh? Not quite. A country is not like a large company or even a political party. It is much too complex to be managed from the top down, and a control freak is bound to flounder. The approach needed is very different.

Some tasks of governance, it is true, are tailor-made for efficient managers. Building infrastructure, taking care of roads and power, building toilets (even without an underlying drainage system) and PR campaigns can all be executed by good managers. But the deeper tasks of making an economy flourish require a different approach. They need a light touch, not a heavy hand.

The 20th century is full of cautionary tales that show that economies cannot be centrally planned from the top down. Examples of that ‘fatal conceit’, to use my hero Friedrich Hayek’s term, include the Soviet Union, Mao’s China, and even the lady Modi most reminds me of, Indira Gandhi.

The task of the state, when it comes to the economy, is to administer a strong rule of law, and to make sure it is applied equally. No special favours to cronies or special interest groups. Just unleash the natural creativity of the people, and don’t try to micro-manage.

Sadly, the BJP’s impulse, like that of most governments of the past, is a statist one. India should have a small state that does a few things well. Instead, we have a large state that does many things badly, and acts as a parasite on its people.

As it happens, the few things that we should do well are all right up Shah’s managerial alley. For example, the rule of law is effectively absent in India today, especially for the poor. As Home Minister, Shah could fix this if he applied the same zeal to governing India as he did to growing the BJP. But will he?

And here we come to the question of incentives. What drives Amit Shah: maximising power, or serving the nation? What is good for the country will often coincide with what is good for the party – but not always. When they diverge, which path will Shah choose? So much rests on that.

The India Uncut Blog © 2010 Amit Varma. All rights reserved.
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I’m not talking about carnival funhouse mirrors, but rather the different options for mirroring symbols, vias, and bond fingers in your IC Package layout. The Allegro Package Designer Plus and SiP Layout tools have two distinct styles of m...(read more)

Hi There,

The variant bom I set gone dissapear. Is there any way to recover this back from the old design file? 

This is the second time it happen to me. Not really sure what could cause this. 

Thanks,

Pornchai

I want to write a transition coverage on an enumeration. One of the parts of that transition is a queue of the enum. I construct this queue in my constructor. Considering the example below, how would one go about it.

In my coverage bin I can create a range like this A => [queue1Enum[0]:queue1Enum[$]] => [queue2Enum[0]:queue2Enum[$]]. But I only get first and last element then.

typedef enum { red, d_green, d_blue, e_yellow, e_white, e_black } Colors;
 Colors dColors[$];
 Colors eColors[$];
 Lcolors = Colors.first();
 do begin
  if (Lcolors[0].name=='d') begin
   dColors.push_back(Lcolors);
  end
  if (Lcolors[0].name=='e') begin
   eColors.push_back(Lcolors);
  end
 end while(Lcolors != Lcolors.first())

 covergroup cgTest with function sample(Colors c);
   cpTran : coverpoint c{
      bins t[] = (red => dColors =>eColors);   
   }
 endgroup

Let’s review a key characteristic feature of Cadence Liberate AMS Mixed-Signal Characterization that offers to you ease of use along with many other benefits like automation of standard Liberty model creation and improvement of up to 20X throughput.(read more)

Key Findings:  Nearly 100% of SoCs are mixed-signal to some extent.  Every one of these could benefit from the use of a metrics-driven unified verification methodology for mixed-signal (MD-UVM-MS), but the modeling step is the biggest hurdle to overcome.  Without the magical models, the process breaks down for lack of performance, or holes in the chip verification.

In the last installment of The Low Road, we were at the mixed-signal verification party. While no one talked about it, we all saw it: The party was raging and everyone was having a great time, but they were all dancing around that big elephant right in the middle of the room. For mixed-signal verification, that elephant is named Modeling.

To get to a fully verified SoC, the analog portions of the design have to run orders of magnitude faster than the speediest SPICE engine available. That means an abstraction of the behavior must be created. It puts a lot of people off when you tell them they have to do something extra to get done with something sooner. Guess what, it couldn’t be more true. If you want to keep dancing around like the elephant isn’t there, then enjoy your day. If you want to see about clearing the pachyderm from the dance floor, you’ll want to read on a little more….

Figure 1: The elephant in the room: who’s going to create the model?

 Whose job is it?

Modeling analog/mixed-signal behavior for use in SoC verification seems like the ultimate hot potato.  The analog team that creates the IP blocks says it doesn't have the expertise in digital verification to create a high-performance model. The digital designers say they don’t understand anything but ones and zeroes. The verification team, usually digitally-centric by background, are stuck in the middle (and have historically said “I just use the collateral from the design teams to do my job; I don’t create it”).

If there is an SoC verification team, then ensuring that the entire chip is verified ultimately rests upon their shoulders, whether or not they get all of the models they need from the various design teams for the project. That means that if a chip does not work because of a modeling error, it ought to point back to the verification team. If not, is it just a “systemic error” not accounted for in the methodology? That seems like a bad answer.

That all makes the most valuable guy in the room the engineer, whose knowledge spans the three worlds of analog, digital, and verification. There are a growing number of “mixed-signal verification engineers” found on SoC verification teams. Having a specialist appears to be the best approach to getting the job done, and done right.

So, my vote is for the verification team to step up and incorporate the expertise required to do a complete job of SoC verification, analog included. (I know my popularity probably did not soar with the attendees of DVCON with that statement, but the job has to get done).

It’s a game of trade-offs

The difference in computations required for continuous time versus discrete time behavior is orders of magnitude (as seen in Figure 2 below). The essential detail versus runtime tradeoff is a key enabler of verification techniques like software-driven testbenches. Abstraction is a lossy process, so care must be taken to fully understand the loss and test those elements in the appropriate domain (continuous time, frequency, etc.).

Figure 2: Modeling is required for performance

AFE for instance

The traditional separation of baseband and analog front-end (AFE) chips has shifted for the past several years. Advances in process technology, analog-to-digital converters, and the desire for cost reduction have driven both a re-architecting and re-partitioning of the long-standing baseband/AFE solution. By moving more digital processing to the AFE, lower cost architectures can be created, as well as reducing those 130 or so PCB traces between the chips.

There is lots of good scholarly work from a few years back on this subject, such as Digital Compensation of Dynamic Acquisition Errors at the Front-End of ADCS and Digital Compensation for Analog Front-Ends: A New Approach to Wireless Transceiver Design.

Figure 3: AFE evolution from first reference (Parastoo)

The digital calibration and compensation can be achieved by the introduction of a programmable solution. This is in fact the most popular approach amongst the mobile crowd today. By using a microcontroller, the software algorithms become adaptable to process-related issues and modifications to protocol standards.

However, for the SoC verification team, their job just got a whole lot harder. To determine if the interplay of the digital control and the analog function is working correctly, the software algorithms must be simulated on the combination of the two. That is, here is a classic case of inseparable mixed-signal verification.

So, what needs to be in the model is the big question. And the answer is, a lot. For this example, the main sources of dynamic error at the front-end of ADCs are critical for the non-linear digital filtering that is highly frequency dependent. The correction scheme must be verified to show that the nonlinearities are cancelled across the entire bandwidth of the ADC. 

This all means lots of simulation. It means that the right level of detail must be retained to ensure the integrity of the verification process. This means that domain experience must be added to the list of expertise of that mixed-signal verification engineer.

Back to the pachyderm

There is a lot more to say on this subject, and lots will be said in future posts. The important starting point is the recognition that the potential flaw in the system needs to be examined. It needs to be examined by a specialist.  Maybe a second opinion from the application domain is needed too.

So, put that cute little elephant on your desk as a reminder that the beast can be tamed.

Steve Carlson

Related stories

- It’s Late, But the Party is Just Getting Started

Key Findings: Advantest, in mixed-signal SoC design, sees 50X speedup, 25 day test reduced to 12 hours, dramatic test coverage increase.

Trolling through the CDNLive archives, I discovered another gem. At the May 2013 CDNLive in Munich, Thomas Henkel and Henriette Ossoinig of Advantest presented a paper titled “Timing-accurate emulation of a mixed-signal SoC using Palladium XP”. Advantest makes advanced electronics test equipment. Among the semiconductor designs they create for these products is a test processor chip with over 100 million logic transistors, but also with lots of analog functions.They set out to find a way to speed up their full-chip simulations to a point where they could run the system software. To do that, they needed about a 50X speed-up. Well, they did it!

Figure 1: Advantest SoC Test Products

To skip the commentary, read Advantest's paper here. 

Problem Statement

Software is becoming a bigger part of just about every hardware product in every market today, and that includes the semiconductor test market. To achieve high product quality in the shortest amount of time, the hardware and software components need to be verified together as early in the design cycle as possible. However, the throughput of a typical software RTL simulation is not sufficient to run significant amounts of software on a design with hundreds of millions of transistors.  

Executing software on RTL models of the hardware means long runs  (“deep cycles”) that are a great fit for an emulator, but the mixed-signal content posed a new type of challenge for the Advantest team.  Emulators are designed to run digital logic. Analog is really outside of the expected use model. The Advantest team examined the pros and cons of various co-simulation and acceleration flows intended for mixed signal and did not feel that they could possibly get the performance they needed to have practical runtimes with software testbenches. They became determined to find a way to apply their Palladium XP platform to the problem.

Armed with the knowledge of the essential relationship between the analog operations and the logic and software operations, the team was able to craft models of the analog blocks using reduction techniques that accurately depicted the essence of the analog function required for hardware-software verification without the expense of a continuous time simulation engine.

The requirements boiled down to the following:

• Generation of digital signals with highly accurate and flexible timing

• Complete chip needs to run on Palladium XP platform

• Create high-resolution timing (100fs) with reasonable emulation performance, i.e. at least 50X faster than simulation on the fastest workstations

Solution Idea

The solution approach chosen was to simplify the functional model of the analog elements of the design down to generation of digital signal edges with high timing accuracy. The solution employed a fixed-frequency central clock that was used as a reference.Timing-critical analog signals used to produce accurately placed digital outputs were encoded into multi-bit representations that modeled the transition and timing behavior. A cell library was created that took the encoded signals and converted them to desired “regular signals”. 

Automation was added to the process by changing the netlisting to widen the analog signals according to user-specified schematic annotations. All of this was done in a fashion that is compatible with debugging in Cadence’s Simvision tool.  Details on all of these facets to follow.

The Timing Description Unit (TDU) Format

The innovative thinking that enabled the use of Palladium XP was the idea of combining a reference clock and quantized signal encoding to create offsets from the reference. The implementation of these ideas was done in a general manner so that different bit widths could easily be used to control the quantization accuracy.

Figure 2: Quantization method using signal encoding

Timed Cell Modeling

You might be thinking – timing and emulation, together..!?  Yes, and here’s a method to do it….

The engineering work in realizing the TDU idea involved the creation of a library of cells that could be used to compose the functions that convert the encoded signal into the “real signals” (timing-accurate digital output signals). Beyond some basic logic cells (e.g., INV, AND, OR, MUX, DFF, TFF, LATCH), some special cells such as window-latch, phase-detect, vernier-delay-line, and clock-generator were created. The converter functions were all composed from these basic cells. This approach ensured an easy path from design into emulation.

The solution was made parameterizable to handle varying needs for accuracy.  Single bit inputs need to be translated into transitions at offset zero or a high or low coding depending on the previous state.  Single bit outputs deliver the final state of the high-resolution output either at time zero, the next falling, or the next rising edge of the grid clock, selectable by parameter. Output transitions can optionally be filtered to conform to a configurable minimum pulse width.

Timed Cell Structure

There are four critical elements to the design of the conversion function blocks (time cells):

                Input conditioning – convert to zero-offset, optional glitch preservation, and multi-cycle path

                Transition sorting – sort transitions according to timing offset and specified precedence

                Function – for each input transition, create appropriate output transition

                Output filtering – Capability to optionally remove multiple transitions, zero-width, pulses, etc.

Timed Cell Caveat

All of the cells are combinational and deliver a result in the same cycle of an input transition. This holds for storage elements as well. For example a DFF will have a feedback to hold its state. Because feedback creates combinational loops, the loops need a designation to be broken (using a brk input conditioning function in this case – more on this later). This creates an additional requirement for flip-flop clock signals to be restricted to two edges per reference clock cycle.

Note that without minimum width filtering, the number of output transitions of logic gates is the sum of all input transitions (potentially lots of switching activity). Also note that the delay cell has the effect of doubling the number of output transitions per input transition.

Figure 3: Edge doubling will increase switching during execution

SimVision Debug Support

The debug process was set up to revolve around VCD file processing and directed and viewed within the SimVision debug tool. In order to understand what is going on from a functional standpoint, the raw simulation output processes the encoded signals so that they appear as high-precision timing signals in the waveform viewer. The flow is shown in the figure below.

Figure 4: Waveform post-processing flow

The result is the flow is a functional debug view that includes association across representations of the design and testbench, including those high-precision timing signals.

Figure 5: Simvision debug window setup

Overview of the Design Under Verification (DUV)

Verification has to prove that analog design works correctly together with the digital part. The critical elements to verify include:

• Programmable delay lines move data edges with sub-ps resolution

• PLL generates clocks with wide range of programmable frequency

• High-speed data stream at output of analog is correct

These goals can be achieved only if parts of the analog design are represented with fine resolution timing.

Figure 6: Mixed-signal design partitioning for verification

How to Get to a Verilog Model of the Analog Design

There was an existing Verilog cell library with basic building blocks that included:

- Gates, flip-flops, muxes, latches

- Behavioral models of programmable delay elements, PLL, loop filter, phase detector

With a traditional simulation approach, a cell-based netlist of the analog schematic is created. This netlist is integrated with the Verilog description of the digital design and can be simulated with a normal workstation. To use Palladium simulation, the (non-synthesizable) portions of the analog design that require fine resolution timing have to be replaced by digital timing representation. This modeling task is completed by using a combination of the existing Verilog cell library and the newly developed timed cells.

Loop Breaking

One of the chief characteristics of the timed cells is that they contain only combinational cells that propagate logic from inputs to outputs. Any feedback from a cell’s transitive fanout back to an input creates a combinational loop that must be broken to reach a steady state. Although the Palladium XP loop breaking algorithm works correctly, the timed cells provided a unique challenge that led to unpredictable results.  Thus, a process was developed to ensure predictable loop breaking behavior. The user input to the process was to provide a property at the loop origin that the netlister recognized and translated to the appropriate loop breaking directives.

Augmented Netlisting

Ease of use and flow automation were two primary considerations in creating a solution that could be deployed more broadly. That made creating a one-step netlisting process a high-value item. The signal point annotation and automatic hierarchy expansion of the “digital timing” parameter helped achieve that goal. The netlister was enriched to identify the key schematic annotations at any point in the hierarchy, including bit and bus signals.

Consistency checking and annotation reporting created a log useful in debugging and evolving the solution.

Wrapper Cell Modeling and Verification

The netlister generates a list of schematic instances at the designated “netlister stop level” for each instance the requires a Verilog model with fine resolution timing. For the design in this paper there were 160 such instances.

The library of timed cells was created; these cells were actually “wrapper” cells comprised of the primitives for timed cell modeling described above. A new verification flow was created that used the behavior of the primitive cells as a reference for the expected behavior of the composed cells. The testing of the composed cells included had the timing width parameter set to 1 to enable direct comparison to the primitive cells. The Cadence Incisive Enterprise Simullator tool was successfully employed to perform assertion-based verification of the composed cells versus the existing primitive cells.

Mapping and Long Paths

Initial experiments showed that inclusion of the fine resolution timed cells into the digital emulation environment would about double the required capacity per run. As previously pointed out, the timed cells having only combinational forward paths creates a loop issue. This fact also had the result of creating some such paths that were more than 5,000 steps of logic. A timed cell optimization process helped to solve this problem. The basic idea was to break the path up by adding flip-flops in strategic locations to reduce combinational path length. The reason that this is important is that the maximum achievable emulation speed is related to combinational path length.

Results

Once the flow was in place, and some realistic test cases were run through it, some further performance tuning opportunities were discovered to additionally reduce runtimes (e.g., Palladium XP tbrun mode was used to gain speed). The reference used for overall speed gains on this solution was versus a purely software-based solution on the highest performance workstation available.

The findings of the performance comparison were startlingly good:

• On Palladium XP, the simulation speed is 50X faster than on Advantest’s fastest workstation

• Software simulation running 25 days can now be run in 12 hours -> realistic runtime enables long-running tests that were not feasible before

• Now have 500 tests that execute once in more than 48 hours

• They can be run much more frequently using randomization and this will increase test coverage dramatically

Steve Carlson