1 Netherlands Antillean Guilder = 0.449 British Pound Sterling

1 Netherlands Antillean Guilder = 1.255 Fiji Dollar

1 Netherlands Antillean Guilder = 0.5078 Euro

1 Netherlands Antillean Guilder = 8.6696 Egyptian Pound

1 Netherlands Antillean Guilder = 7.9448 Estonian Kroon

1 Netherlands Antillean Guilder = 71.4884 Algerian Dinar

1 Netherlands Antillean Guilder = 30.6598 Dominican Peso

1 Netherlands Antillean Guilder = 3.8329 Danish Krone

1 Netherlands Antillean Guilder = 13.9999 Czech Republic Koruna

1 Netherlands Antillean Guilder = 316.9212 Costa Rican Colon

1 Netherlands Antillean Guilder = 2170.5102 Colombian Peso

1 Netherlands Antillean Guilder = 3.9406 Chinese Yuan Renminbi

1 Netherlands Antillean Guilder = 460.0067 Chilean Peso

1 Netherlands Antillean Guilder = 0.5409 Swiss Franc

1 Netherlands Antillean Guilder = 0.7808 Canadian Dollar

1 Netherlands Antillean Guilder = 6.7649 Botswana Pula

1 Netherlands Antillean Guilder = 3.1932 Brazilian Real

1 Netherlands Antillean Guilder = 3.8412 Bolivian Boliviano

1 Netherlands Antillean Guilder = 0.7872 Brunei Dollar

1 Netherlands Antillean Guilder = 0.2107 Bahraini Dinar

1 Netherlands Antillean Guilder = 1.0057 Bulgarian Lev

1 Netherlands Antillean Guilder = 47.3454 Bangladeshi Taka

1 Netherlands Antillean Guilder = 0.8525 Australian Dollar

1 Netherlands Antillean Guilder = 37.0281 Argentine Peso

1 Netherlands Antillean Guilder = 2.0461 United Arab Emirates Dirham

1 Estonian Kroon = 0.1259 Netherlands Antillean Guilder

1 Danish Krone = 0.2609 Netherlands Antillean Guilder

1 Fiji Dollar = 0.7968 Netherlands Antillean Guilder

1 New Zealand Dollar = 1.1019 Netherlands Antillean Guilder

1 Croatian Kuna = 0.2587 Netherlands Antillean Guilder

1 Peruvian Nuevo Sol = 0.5281 Netherlands Antillean Guilder

Lawrence, Kansas – The Haskell men's golf team finished 9th out of 11 teams in the Baker Tournament held at Eagle Bend Golf Course in Lawrence, Kansas on Monday and Tuesday. The Indians finished with a round scores of 330, 332, and 325 with a total team score 987.

LAWRENCEVILLE, Ga. – The Association of Independent Institutions (A.I.I.) announced on Monday that Grant Shorty (SO/Albuquerque, NM) of Haskell Indian Nations University (Kan.) has been named the A.I.I.'s Men's Golfer of the Week for the duration of April 10-16.

1 Dominican Peso = 0.0326 Netherlands Antillean Guilder

1 Papua New Guinean Kina = 0.5233 Netherlands Antillean Guilder

1 Brunei Dollar = 1.2703 Netherlands Antillean Guilder

I have completed physical design of a block in innovus. I want to extract rc of that block using quantus .  It will be very helpful if you give step by step procedure and command to run quantus to extract rc of that block.

Cadence ® Spectre ® AMS Designer is a high-performance mixed-signal simulation system. The ability to use multiple engines, and drive from a variety of platforms enables you to "rev...

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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

Hi, 

I want my layout verified by Quantus QRC. But, I can't find the tool bar on the option list ( as show in the picture)

I have tried to install EXT182 and configured it with iscape already, and also make some path settings on .bashrc, .cshrc. But, when I re-source .cshrc and run virtuoso again, I just can't find the toolbar. 

If you have some methods, please let me know.

Thanks a lot!

Appreciated

My virtuoso version is: ICADV12.3

Hello,

I am using Virtuoso 6.1.7.

I am performing the parasitic extraction of a MOM cap array of 32 caps. I use Quantus QRC and I enable field solver. I select “QRCFS” for field solver type and “High” for field solver accuracy. The unit MOM cap is horizontally and vertically symmetric. The array looks like the sketch below and there are no other structures except the unit caps:

Rationally speaking, the capacitance values of the unit caps should be symmetric with respect to a vertical symmetry axis that is between cap16 and cap17 (shown with dashed red line). For example,

the capacitance of cap1 should be equal to the capacitance of cap32

the capacitance of cap2 should be equal to the capacitance of cap31

etc. as there are no other structures around the caps that might create some asymmetry.

Nevertheless, what I observe is the following after the parasitic extraction:

As it can be seen, the result is not symmetric contrary to what is expected. I should also add that I do not observe this when I perform parasitic extraction with no filed solver.

Why do I get this result? Is it an artifact resulting from the field solver tool (my conclusion was yes but still it must be verified)? If not, how can something like this happen?

Many thanks in advance.

Best regards,

Can

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

Verification is the top challenge in mixed-signal design. Bringing analog and digital domains together into unified verification planning, simulating, and debugging is a challenging task for rapidly increasing size and complexity of mixed-signal designs. To more completely verify functionality and performance of a mixed-signal SoC and its AMS IP blocks used to build it, verification teams use simulations at transistor, analog behavioral and real-number model (RNM) and RTL levels, and combination of these.

In recent years, RNM and simulation is being adopted for functional verification by many, due to advantages it offers including simpler modeling requirements and much faster simulation speed (compared to a traditional analog behavioral models like Verilog-A or VHDL-AMS). Verilog-AMS with its wreal continue to be popular choice. Standardization of real number extensions in SystemVerilog (SV) made SV-RNM an even more attractive choice for MS SoC verification.

Verilog-AMS/wreal is scalar real type. SV-RNM offers a powerful ability to define complex data types, providing a user-defined structure (record) to describe the net value. In a typical design, most analog nodes can be modeled using a single value for passing a voltage (or current) from one module to another. The ability to pass multiple values over a net can be very powerful when, for example, the impedance load impact on an analog signal needs to be modeled. Here is an example of a user-defined net (UDN) structure that holds voltage, current, and resistance values:

When there are multiple drives on a single net, the simulator will need a resolution function to determine the final net value. When the net is just defined as a single real value, common resolution functions such as min, max, average, and sum are built into the simulator.  But definition of more complex structures for the net also requires the user to provide appropriate resolution functions for them. Here is an example of a net with three drivers modeled using the above defined structural elements (a voltage source with series resistance, a resistive load, and a current source):

To properly solve for the resulting output voltage, the resolution function for this net needs to perform Norton conversion of the elements, sum their currents and conductances, and then calculate the resolved output voltage as the sum of currents divided by sum of conductances.

With some basic understanding of circuit theory, engineers can use SV-RNM UDN capability to model electrical behavior of many different circuits. While it is primarily defined to describe source/load impedance interactions, its use can be extended to include systems including capacitors, switching circuits, RC interconnect, charge pumps, power regulators, and others. Although this approach extends the scope of functional verification, it is not a replacement for transistor-level simulation when accuracy, performance verification, or silicon correlation are required:  It simply provides an efficient solution for discretely modeling small analog networks (one to several nodes).  Mixed-signal simulation with an analog solver is still the best solution when large nonlinear networks must be evaluated.

Cadence provides a tutorial on EEnet usage as well as the package (EEnet.pkg) with UDN definitions and resolution functions and modeling examples. To learn more, please login to your Cadence account to access the tutorial.

This blog talks about how to enable the AMS Designer flex mode.(read more)

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Shaktikanta Das: દુનિયામાં સૌથી મોટી મંદીનું અનુમાન, 1929 બાદ સૌથી મોટું આર્થિક સંકટ