Pediatric training programs use simulation for procedural skills training. Research demonstrates student satisfaction with simulation training, improved confidence, and improved skills when retested on a simulator. Few studies, however, have investigated the clinical impact of simulation education.

This is the first multicenter, randomized trial to evaluate the impact of simulation-based mastery learning on clinical procedural performance in pediatrics. A single simulation-based training session was not sufficient to improve interns’ clinical procedural performance. (Read the full article)

Practitioners caring for critically ill infants need to acquire competence in insertion of chest tubes for pneumothorax. Ethical and logistic concerns inhibit the use of animals, and there are no realistic simulation models available for neonatal chest tube insertion training.

An inexpensive, nonanimal chest tube insertion model can be easily constructed and used effectively to train interns and residents to improve their knowledge, clinical skills, and comfort levels to perform the chest tube insertion procedure in infants. (Read the full article)

High-fidelity simulation improves individual skills in neonatal resuscitation. Usually, training is performed in a simulation center. Little is known about the impact of in situ training on overall team performance.

In situ high-fidelity simulation training of 80% of a maternity’s staff significantly improved overall team performance in neonatal resuscitation (technical skills and teamwork). Fewer hazardous events occurred, and delay in improving the heart rate was shorter. (Read the full article)

Trainee success rates with infant lumbar puncture are poor. The model of just-in-time learning via simulation has produced clinical improvement for other medical skills such as cardiac compressions and central line dressing changes.

This is the first study to evaluate the impact of just-in-time-and-place simulation-based learning on success with infant lumbar puncture. The intervention improved clinical behaviors associated with success without making a significant impact on success with the procedure. (Read the full article)

Simulation-based education is increasing but its use in pediatric emergency medicine (PEM) fellowships has not been recently documented. Previous studies identified barriers including equipment and space, but growth of simulation centers and equipment has been widespread.

Simulation is widely used in PEM fellowships, and current barriers include faculty and learner time, implementation of best practices in simulation; equipment is less significant. Future work should focus on curriculum and evaluation development, aligning with the milestones. (Read the full article)

The frequency of cardiac arrest is significantly lower in children than in adults, rendering the delivery of high-quality cardiopulmonary resuscitation more difficult. Metronome-based studies in adults showed improvement in adequate compression rate, with a detrimental effect on the depth of chest compressions.

This is the first pediatric study to confirm that the use of a metronome during cardiopulmonary resuscitation significantly improves the delivery of adequate rate without affecting the compression depth. This effect was more prominent among medical students and pediatric residents and fellows than nurses. (Read the full article)

Lehigh Valley's Practical Nursing Program has moved to the Shadow Health Digital Clinical Experience so students can continue clinical rotations through virtual simulations.

Michael Hillman, L. Robert and Mary L. Kimball Assistant Professor of Civil and Environmental Engineering at Penn State, received a NSF CAREER Award to develop new computational methods to simulate fracture.

Review of: Multi-Agent-Based Simulation XIX. 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers by Davidsson, Paul, Verhagen, Harko (Eds.), reviewed by Patrycja Antosz

Volker Grimm, Steven F. Railsback, Christian E. Vincenot, Uta Berger, Cara Gallagher, Donald L. DeAngelis, Bruce Edmonds, Jiaqi Ge, Jarl Giske, Jürgen Groeneveld, Alice S.A. Johnston, Alexander Milles, Jacob Nabe-Nielsen, J. Gareth Polhill, Viktoriia Radchuk, Marie-Sophie Rohwäder, Richard A. Stillman, Jan C. Thiele and Daniel Ayllón: The Overview, Design concepts and Details (ODD) protocol for describing Individual- and Agent-Based Models (ABMs) is now widely accepted and used to document such models in journal articles. As a standardized document for providing a consistent, logical and readable account of the structure and dynamics of ABMs, some research groups also find it useful as a workflow for model design. Even so, there are still limitations to ODD that obstruct its more widespread adoption. Such limitations are discussed and addressed in this paper: the limited availability of guidance on how to use ODD; the length of ODD documents; limitations of ODD for highly complex models; lack of sufficient details of many ODDs to enable reimplementation without access to the model code; and the lack of provision for sections in the document structure covering model design rationale, the model’s underlying narrative, and the means by which the model’s fitness for purpose is evaluated. We document the steps we have taken to provide better guidance on: structuring complex ODDs and an ODD summary for inclusion in a journal article (with full details in supplementary material; Table 1); using ODD to point readers to relevant sections of the model code; update the document structure to include sections on model rationale and evaluation. We also further advocate the need for standard descriptions of simulation experiments and argue that ODD can in principle be used for any type of simulation model. Thereby ODD would provide a lingua franca for simulation modelling.

When I enable clock gating in my synthesis flow (using Genus 18.15), my simulation (using Xcelium) on the post-synthesis netlist fails. The simulation succeeds pre-synthesis and also if I remove clock-gating in the design. I use set_db  lp_insert_clock_gating true to enable clock gating during synthesis. I printed out some of the signals from the netlist and can see where it fails (it incorrectly writes a register). However, I am not sure how to solve this issue or what I should be looking for. Any help would be appreciated. Thanks.

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)

Hi All, Here's another great new feature that I've found very helpful... Broadband SPICE is a new tool for S-parameter simulation in Spectre RF. In the MMSIM13.1.1 ( MMSIM13.1 USR1) release (now available on http://downloads.cadence.com), a...(read more)

Did you check out the new Pnoise and Hbnoise Choosing Analyses forms in the MMSIM 15.1 and IC6.1.7 /ICADV12.2 releases? These forms have been significantly improved and simplified. The Direct Plot Form has also been enhanced and is much easy to use....(read more)

Hi there,

I'm running a transient simulation, and I want to get all instances with model implementation hisim_hv because after that I want to process the data and to adjust some parameters for this kind of devices before dumping the values.

What is the easiest/fastest way to get those instances in skill/ocean?

What I did until now: 

- save the final OP of the simulation and then in skill

openResults()
selectResults('tranOp)
report(?type "hisim_hv" ?param "vgs")

Output seems to be promising, and looks like I can redirect it to a file and after that I have to parse the file.

Is there other simple way? I mean to not save data to file and to parse it.

Eventually having an instance name, is it possible to get the model implementation (hsim_hv, bsim4, etc..)? 

Best Regards,

Marcel

There is no better way other than a self-help training kit -- (rapid adoption kit, or RAK) -- to demonstrate the Incisive Enterprise Simulator's IEEE 1801 / UPF low-power features and its usage. The features include:

• Unique SimVision debugging 
• Patent-pending power supply network visualization and debugging
• Tcl extensions for LP debugging
• Support for Liberty file power description
• Standby mode support
• Support for Verilog, VHDL, and mixed language
• Automatic understanding of complex feedthroughs
• Replay of initial blocks
• ‘x' corruption for integers and enumerated types
• Automatic understanding of loop variables
• Automatic support for analog interconnections

Mickey Rodriguez, AVS Staff Solutions Engineer has developed a low power UPF-based RAK, which is now available on Cadence Online Support for you to download.

• This rapid adoption kit illustrates Incisive Enterprise Simulator (IES) support for the IEEE 1801 power intent standard. 

Patent-Pending Power Supply Network Browser. (Only available with the LP option to IES)

• In addition to an overview of IES features, SimVision and Tcl debug features, a lab is provided to give the user an opportunity to try these out.

The complete RAK and associated overview presentation can be downloaded from our SoC and Functional Verification RAK page:

We are covering the following technologies through our RAKs at this moment:

Synthesis, Test and Verification flow
Encounter Digital Implementation (EDI) System and Sign-off Flow
Virtuoso Custom IC and Sign-off Flow
Silicon-Package-Board Design
Verification IP
SOC and IP level Functional Verification
System level verification and validation with Palladium XP

Please visit https://support.cadence.com/raks to download your copy of RAK.

We will continue to provide self-help content on Cadence Online Support, your 24/7 partner for learning more about Cadence tools, technologies, and methodologies as well as getting help in resolving issues related to Cadence software. If you are signed up for e-mail notifications, you're likely to notice new solutions, application notes (technical papers), videos, manuals, etc.

Note: To access the above documents, click a link and use your Cadence credentials to log on to the Cadence Online Support https://support.cadence.com/ website.

Happy Learning!

Sumeet Aggarwal and Adam Sherer

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

Recently, I meet the simulation error as the picture shows when I simulate my circuit with transient.  how can I solve this problem?

thank you~

Hi All,

I saw the cadence tutorial on measuring IIP3 with 3 tones test (Lets say I have a mixer in the test so two tones are entered in the RF port and one is the LO).

Now, I would like to verify if my receiver meets the bluetooth standard. In the standard it says to enter a signal at -64dBm and two additional signals (interference) 

at -39dBm each which placed one k (lets say k equals to one for the example) channels apart and the other 2k channels apart (so 3 signals enter the RF port). These signals cause an intermodulation product to fall

at the frequency of the desired signal. I would like to measure the IIP3 in this case. 

Now, I need to enter 4 tones and the IIP3 is measured (based on cadence tutorial) using sweep in the hb.

I do not want to sweep power since I need to enter exact power. I tried to use multi sinusoidal option in the port with exact power but it does not work.

How in general am I be able to check communication standard in this way using virtuoso and measure IIP3?

Can someone please help me?

Thanks in advance! 

Hi, when I applied a voltage divider implemented by two 100-ohm resistors to a 2Vpp 5GHz vsin source, the phase noise simulation using pnoise/fullspectrum with different types, jitter and source have different results. The simulated output noise results are 165.76aV2/Hz for pmjitter case, and 828.79zV2/Hz for source case. The source case result equals to the output noise calculation.

For my application, the output will be applied to driven circuits and thus pm jitter is concerned. As the pmjitter is based on the noise sampling at the threshold crossings, I was wondering how spectre gets the pmjitter resullts since sampling white noise with infinite bandwidth is impossible to my knowledge?

Interestingly, the Jee result by integration from 10kHz to 2.5GHz is ~41fs and is closed to Jee,rms from the transient noise simulation. I am also not sure how these results come and match each other. If applying the voltage divider output to drive next stages, I was wondering to what extent I can trust the input jitter from these simulations? Thank you.

Hi,

i'm using spectre HB simulation on PA (Power Amplifier) test_bench to perform large signal analysis (i want to plot Output power vs intput power, PAE and Gain)

Although the simulation returns no error, i still can't plot anything. seem like there is an issue with the ports i'm using. (analoglib ports)

i attach an image of my configuration so maybe you can find something helpful in it. 

thank you all for your help

best regards

hi everyone, 

i'm trying to create a netlist for aging simulation. i would like to simulate how power, Gain and PAE (efficiency) are inlfuenced after 3 hours

i would be grateful if someone can correct my syntax in the netlist since i'm trying to make a sweep HB  simulation where the input power is the parameter.

i did it without any error for the sp (S parameters)  simulation.

you can see the images for both sp and hb simulation netlists. (from left to right: sp aging netlist; hb aging netlist)

i will be grateful if someone can provide me some syntax advices.

thanks,

best regards

I'm sending encrypted HDL to a customer who will use Cadence IES for simulation and was wondering how I should go about the encryption.

Does IES support the IEEE's P1735 and if so, where can I find Cadence's public key for performing the encryption?

Or is there an alternative solution that I can use for encryption?

xmsim is not exiting the simulation for this error. It is unusual for the simulator to not exit for an error. I have just started using uvm and this is occurring during the randomization step for a sequencer item.

xmsim: *E,RNDCNSTE

I am using -EXIT on the command line.

I am using Xcelium 19.03-s013.

Any insights are appreciated. Thanks.

-Jim

hi,

due to technical problem i am running simulation through terminal. Therefore, I have a Verilog file, a test bench and i have also exported from Genus synthesized netlist and sdf file. Now, how can i annotate sdf in my post-synthesis simulation using XCELIUM while using command line?

thank you

Just in time for the holidays, inside the posted tar ball is some code to solve 9x9 Sudoku puzzles with the Assertion-Driven Simulation (ADS) capability of Incisive Enterprise Verifier (IEV). Enjoy! Joerg Mueller Solutions Engineer for Team Verify

Hello,

 I am working in a digital design. The functional, post synthesis and post P&R without IO pads are all working fine, i.e., functionally and with clean timing reports "no setup/hold violations". I just added the IO pads to the same design, I had to change the timing constraints a bit for the synthesis but I have a clean design at SOC Encounter, i.e., clean DRC and clean timing reports "no setup/hold violations". However, when I perform simulation using the exported net-list from SOC Encounter together with SDF exported from the same tool, I got a lot of hold violations. Consequently, the design is not funcitioning.

Why and how I can overcome or trobleshoot this issue?

In waiting for your feedback and comments.

Regards.

Hi ,

     I have designed analog IP in cadence ADE and simulated in spectre. All corner results looks good. when i run monte carlo 1000 runs have high current in 125C two runs. Simulated with same setup in different user, all clean.Need to know what type sampling method used and why its not clean with my setup.

Thanks,

Anbarasu

Hello,

I am running a transient simulation on my circuit and usually my simulation time took me more than a day (The circuit is quite big). I am usually saving specific nodes to decrease the simulation time. My problem is, since it usually took me one day to finish I need to save my trans simulation just in case something bad happens. I am aware that the transient simulation have the options for save/restore. But, when I tried to use it I have some problem. Whenever I restore the save file, it starts where it ends before (expected function) but my data is incomplete. It doesn't save the previous data. Its kind of my data is incomplete. What I did is set the saveperiod and savefile. I hope someone can help me. Thank you!

Regards,

Kiel

Hello,

I am trying to use calcVal for creating a spec condition from a simulated parameter and although this works perfectly fine in corner simulations, I am having some difficulties in Monte-Carlo (and I will explain).

(I have also read "Using calcVal() and its arguments with ADE Assembler" in Resources > Rapid Adoption Kits but couldn't find any relevant information that would help me address the "issue").

In the above example I am performing a MC simulation which has 2 corners of 10 runs each. I would like to get the minimum value of variable "OC_limit_thres" out of those 10 runs and pass it as my upper limit to a range argument for variable "OC_flag_thres", so the CPK can be calculated.

So the range statement should in reality be like this:

range 32m 44.34m (for corner 0)

range 32m 43.14m (for corner 1)

If I open the Detail - Transpose view in the Results tab, the calcVal("OC_limit_thres" "Currlim_TurnOn_C11") is calculated perfectly fine for each run but here I need one single value out of those 10 runs - in this case the minimum - in order for calcVal to evalute on multiple runs of 1 corner.

How can this be done please?

Thank you in advance for your time.

I put some stimulus in the simulation file section : 

_vpd_data_enb (pu_data_enb 0) vsource wave=[0 0 1n 0 1.015n vcchbm 3n vcchbm] dc=0 type=pwl
_vpu_data_enb (pd_data_enb 0) vsource dc=pu_enb type=dc

I get the following error. 

ERROR (OSSGLD-18): The command character after '[' in the NLP expression '[0 0 1n 0 1.015n vcchbm 3n vcchbm] dc=0 type=pwl

' is not a valid

character. The command character is the first character after '[' in the NLP

expression. It must be '?', '!', '#', '$', 'n', '@', '.', '~' or '+'. Enter a

valid character as the command character.

si: simin did not complete successfully.

I dont see anything wrong with the stimulus syntax

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.

A vulnerability associated with Use of Hard-coded Credentials (CWE-798) exists in Simulation Process Intelligence (3DOrchestrate Services) on premises licensed program. The security risk is evaluated as High (CVSS v.3.0 Base Score 8.0) and affects all 3DEXPERIENCE releases (from 3DEXPERIENCE R2014x to 3DEXPERIENCE R2020x).

A sophisticated computer simulation of the universe, approximately 1 billion light years across, is modelling tens of thousands of galaxies

Google's Sycamore quantum computer, which recently demonstrated its dominance over ordinary computers, is now breaking records in quantum chemistry

Christopher T. Richards and Enrico A. Eberhard

Muscle force-length dynamics are governed by intrinsic contractile properties, motor stimulation and mechanical load. Although intrinsic properties are well-characterised, physiologists lack in vitro instrumentation accounting for combined effects of limb inertia, musculoskeletal architecture and contractile dynamics. We introduce in vitro virtual-reality (in vitro-VR) which enables in vitro muscle tissue to drive a musculoskeletal jumping simulation. In hardware, muscle force from a frog plantaris was transmitted to a software model where joint torques, inertia and ground reaction forces were computed to advance the simulation at 1 kHz. To close the loop, simulated muscle strain was returned to update in vitro length. We manipulated 1) stimulation timing and, 2) the virtual muscle's anatomical origin. This influenced interactions among muscular, inertial, gravitational and contact forces dictating limb kinematics and jump performance. We propose that in vitro-VR can be used to illustrate how neuromuscular control and musculoskeletal anatomy influence muscle dynamics and biomechanical performance.

Introduction

Immunotherapy has become the standard of care in advanced non-small cell lung cancer (NSCLC). We aimed to quantify the economic impact, in France, of anti-PD-1 therapy for NSCLC.

A longstanding awareness in generating resistance to common antimicrobial therapies by Gram-negative bacteria has made them a major threat to global health. The application of antimicrobial peptides as a therapeutic agent would be a great opportunity to combat bacterial diseases. Here, we introduce a new antimicrobial peptide (~8.3 kDa) from probiotic strain Lactobacillus acidophilus ATCC 4356, designated acidocin 4356 (ACD). This multifunctional peptide exerts its anti-infective ability against Pseudomonas aeruginosa through an inhibitory action on virulence factors, bacterial killing, and biofilm degradation. Reliable performance over tough physiological conditions and low hemolytic activity confirmed a new hope for the therapeutic setting. Antibacterial kinetic studies using flow cytometry technique showed that the ACD activity is related to the change in permeability of the membrane. The results obtained from molecular dynamic (MD) simulation were perfectly suited to the experimental data of ACD behavior. The structure-function relationship of this natural compound, along with the results of transmission electron microscopy analysis and MD simulation, confirmed the ability of the ACD aimed at enhancing bacterial membrane perturbation. The peptide was effective in the treatment of P. aeruginosa infection in mouse model. The results support the therapeutic potential of ACD for the treatment of Pseudomonas infections.

IMPORTANCE Multidrug-resistant bacteria are a major threat to global health, and the Pseudomonas bacterium with the ability to form biofilms is considered one of the main causative agents of nosocomial infections. Traditional antibiotics have failed because of increased resistance. Thus, finding new biocompatible antibacterial drugs is essential. Antimicrobial peptides are produced by various organisms as a natural defense mechanism against pathogens, inspiring the possible design of the next generation of antibiotics. In this study, a new antimicrobial peptide was isolated from Lactobacillus acidophilus ATCC 4356, counteracting both biofilm and planktonic cells of Pseudomonas aeruginosa. A detailed investigation was then conducted concerning the functional mechanism of this peptide by using fluorescence techniques, electron microscopy, and in silico methods. The antibacterial and antibiofilm properties of this peptide may be important in the treatment of Pseudomonas infections.

Scientists have developed a method that combines different resolution levels in a computer simulation of biological membranes. Their algorithm backmaps a large-scale model that includes features, such as membrane curvature, to its corresponding coarse-grained molecular model. This has allowed them to zoom in on toxin-induced membrane budding and to simulate a full-sized mitochondrial lipid membrane. Their approach opens the way to whole-cell simulations at a molecular level.

A recent study has described new states that can be found in super-cold atom experiments, which could have applications for quantum technology.

A recent study at the Okinawa Institute of Science and Technology Graduate University has described new states that can be found in super-cold atom experiments, which could have applications for quantum technology.

Scientists from the University of Groningen have developed a method that combines different resolution levels in a computer simulation of biological membranes. Their algorithm backmaps a large-scale model that includes features, such as membrane curvature, to its corresponding coarse-grained molecular model. This has allowed them to zoom in on toxin-induced membrane budding and to simulate a full-sized mitochondrial lipid membrane. Their approach opens the way to whole-cell simulations at a molecular level.

Yokogawa Electric Corporation (TOKYO: 6841) announced today that its subsidiary, Yokogawa Engineering Asia, has been awarded a contract from PUB, Singapore's National Water Agency, to develop a plant simulation system for the Lower Seletar Waterworks, a 60 million gallons per day (mgd) water treatment facility in Singapore. This is part of PUB's continual investments in innovation and R&D to ensure a safe and sustainable water supply for Singapore.

Mar 26, 2020

In this paper, a quantitative Waste to Energy Recovery Assessment (WERA) framework is used to stochastically analyze the feasibility of waste-to-energy systems in selected cities in Asia.

Mar 26, 2020

In this paper, a quantitative Waste to Energy Recovery Assessment (WERA) framework is used to stochastically analyze the feasibility of waste-to-energy systems in selected cities in Asia.

Mar 26, 2020

In this paper, a quantitative Waste to Energy Recovery Assessment (WERA) framework is used to stochastically analyze the feasibility of waste-to-energy systems in selected cities in Asia.

Mar 26, 2020

In this paper, a quantitative Waste to Energy Recovery Assessment (WERA) framework is used to stochastically analyze the feasibility of waste-to-energy systems in selected cities in Asia.