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The Rise of Transaction-Based Emulation

The Rise of Transaction-Based Emulation
by Bernard Murphy on 04-06-2017 at 12:00 pm

One serious challenge to the early promise of accelerating verification through emulation was that, while in theory the emulator could run very fast, options for driving and responding to that fast model were less than ideal. You could use in-circuit emulation (ICE), connecting the emulation to real hardware and allowing you to run fast (with a little help in synchronizing rates between the emulation and the external hardware). But these setups took time, often considerable time, and had poor reliability; at least one connection would go bad every few hours and could take hours to track down.


Alternatively, you could connect to a (software-based) simulator testbench, running on a PC, but that dragged overall performance down to little better than running the whole testbench + DUT (device under test) simulation on the PC. For non-experts in the domain, testbenches mostly run on a PC rather than the emulator, because emulators are designed to deal with synthesizable models, while most testbenches contain logic too complex to be synthesizable. Also emulators are expensive, so even if a testbench can be made synthesizable, there’s a tradeoff between cost and speed.

ICE for all its problems was the only usable option but was limited by cost and value in cases where setup might take nearly as much time as getting first silicon samples. More recently, ICE has improved dramatically in usability and reliability and remains popular for live in-circuit testing. Approaches to software-based testing have also improved dramatically and are also popular where virtualized testing is considered a benefit, and that’s the subject of this blog. (I should note in passing that there are strong and differing views among emulation experts on the relative merits of virtual and ICE-based approaches. I’ll leave that debate to the protagonists.)

There are two primary reasons that simulation-based testbenches are slow – low level (down to signal-level, cycle-accurate) modeling in the testbench and massive amounts of signal-level communication between the testbench and the DUT. Back in the dawn of simulation time, the first problem wasn’t a big deal. Most of the simulation activity was in the DUT and the testbench accounted for a small overhead. But emulation (in principle) reduces DUT time by several orders of magnitude, so time spent in the testbench (and PLI interfaces) becomes dominant. Overall, you get some speed-up but it falls far short of those orders of magnitude you expected.

This problem becomes much worse when you think of the testbenches we build today. These are much more complex thanks to high levels of behavioral modeling and assertion/coverage properties. Now it is common to expect 50-90% of activity in the testbench (that’s why debugging testbenches has become so important); as a result traditional approaches to co-simulation with an emulator show hardly any improvement over pure simulation speeds.
One way to fix this problem is to move up the level of abstraction for the testbench to C/C++. This is a popular trend, especially in software-driven/system level testing where translating tests to SV/UVM may become challenging and arguably redundant. (SV/UVM still plays a role as a bridge to emulation.) Now testbench overhead can drop down to a very small percentage, delivering much more of that promised emulation speedup to total verification performance.

But you still must deal with all that signal communication between testbench and emulator. Now the botteleneck is defined by thousands of signals, each demanding synchronized handling of cycle-accurate state changes. That signal interface complexity also must be abstracted to get maximum return from the testbench/emulator linkage. That’s where the second important innovation comes in – a transaction-based interface. Instead of communicating signal changes, you communicate multi-cycle transactions; this alone could allow for some level of speed-up.

But what really makes the transaction-based interface fly is a clever way to implement communication through the Standard Co-Emulation Modeling Interface (SCE-MI). SCE-MI is an Accellera-defined standard based on the Direct Programming Interface (DPI) extension to the SV standard. This defines a mechanism to communicate directly and portably (without PLI) between an abstracted testbench and an emulator.

The clever part is splitting communication into two functions, one running on the emulator and the other on the PC. On the emulator, you have a synthesizable component assembling and disassembling transactions. On one side, it’s communicating with all those signals from the DUT and can run at emulator speed because it’s synthesized into emulator function primitives. On the other side, it communicates transactions to a proxy function running on the PC.

Now you have fast performance on the emulator, fast (because greatly compressed) communication between PC and emulator, and fast performance in the testbench. All of which makes it possible to rise closer to the theoretical performance that the emulator can offer. It took a bunch of work and a couple of standards but the payback is obvious. What’s more, tests you build should be portable across emulation platforms. Pretty impressive. Mentor has a white-paper which gives more details.


Machine Learning Accelerates Library Characterization by 50 Percent!

Machine Learning Accelerates Library Characterization by 50 Percent!
by Daniel Nenni on 04-06-2017 at 7:00 am

Standard cell, memory, and I/O library characterization is a necessary, but time-consuming, resource intensive, and error-prone process. With the added complexity of advanced and low power manufacturing processes, fast and accurate statistical and non-statistical characterization is challenging, creating the need for a new class of tools to address these challenges.

Many elements within standard cells, memory, and I/O follow similar trends under differing conditions, for example: cell families have similar topology but are just sized differently, differing arcs often show similar behaviors, and PVT (process, voltage, temperature) corners show similar trends but are shifted, scaled or skewed.

Because of these similar trends, library characterization can be accelerated with machine learning technologies. Existing data can be mined for trends, and new library models can be built from previously characterized libraries, but as these library models are built, there is also a need to ensure Monte Carlo accurate LVF/AOCV/POCV library models.

Solido, an EDA industry leader in machine learning technologies, has newly launched ML Characterization Suite–an exciting new tool able to meet these increasing characterization demands. Their continuing success with Variation Designer has placed them in a unique position to develop tools to leverage large scale trends in characterization and mine existing data for trends, all while keeping accuracy paramount.

Within Solido ML Characterization Suite are Predictor and Statistical Characterizer products. Predictor works by:

  • Reading in existing characterized libraries,
  • Determining the PVT corner conditions and turning them into variables and values,
  • Building regression models to predict Liberty values at other PVT conditions,
  • Writing out new Liberty files at new PVT corners.


Solido ML Characterization Suite Predictor has been shown by customers in production to reduce library characterization runtimes by 30% to 70% without compromising accuracy. It’s now instantly possible to produce more libraries for additional PVT corners, saving days to weeks in characterization time and saving on SPICE simulator and characterization resources. Predictor is easy to add into any characterization flow, and works with all Liberty data – NLDM, CCS, CCSN, AOCV, LVF, ECSM, etc.

The second component in ML Characterization Suite is Statistical Characterizer. Statistical Characterizer works by:

  • Reading existing libraries without LVF/AOCV/POCV statistical data,
  • Selecting simulations to produce accurate LVF/AOCV/POCV data for all corners and to parallelize simulations efficiently,
  • Adaptively selecting additional simulations where more accuracy is needed,
  • Writing existing libraries with LVF/AOCV/POCV added.


This technique generates statistical library models with Monte Carlo and SPICE accuracy over 1000 times faster than brute force Monte Carlo. Statistical Characterizer also precisely handles non-Gaussian distributions, all while delivering true 3-sigma LVF/AOCV/POCV.

For even greater statistical characterization savings, Statistical Characterizer and Predictor can be used in tandem for fast, accurate statistical characterization. By using Statistical Characterizer only on anchor corners to quickly add Monte Carlo accurate LVF/AOCV/POCV, then running Predictor to create the remaining corners, Statistical Characterizer only needs to be run on half of the PVT conditions.

ML Characterization Suite Predictor and Statistical Characterizer are available immediately. Sign up here for a 15min demo.

About Solido Design Automation
Solido Design Automation Inc. is a leading provider of variation-aware design software for high yield and performance IP and systems-on-a-chip (SOCs). Solido plays an essential role in de-risking the variation impacts associated with the move to advanced and low-power processes, providing design teams improved power, performance, area and yield for memory, standard cell, analog/RF, and custom digital design. Solido’s efficient software solutions address the exponentially increasing analysis required without compromising time-to-market. The privately held company is venture capital funded and has offices in the USA, Canada, Asia and Europe. For further information, visit www.solidodesign.com or call 306-382-4100.


Integrated Photonics Accelerates with Entrance of TSMC and TowerJazz Foundries

Integrated Photonics Accelerates with Entrance of TSMC and TowerJazz Foundries
by Mitch Heins on 04-05-2017 at 12:00 pm


I’m writing this from the Boston airport on my way home from four straight weeks of PIC (photonic integrated circuit) related travel. It’s been a grueling but very rewarding four weeks and the big take away from this month is that there are now many more signs in the market that integrated photonics is reaching a real tipping point.

I started off March by traveling to Brussels, Belgium to attend the PIC International Conference. This was PIC International’s second year and attendance grew from 440 attendees last year to over 550 this year. This was echoed at the Optical Fiber Conference held in Los Angeles which boasted 14,500 attendees and over 663 exhibitors. The conference was packed with talks about how the industry is girding for the explosive data growth expected to be driven by IoT and 5G cellular. Another key indicator of growing momentum was a 30% increase in attendance of conference short courses meant to educate professionals on the technical aspects of photonics.

The real buzz however, came with several noteworthy news items in March. Among them was a press release by Luxtera where they announced they will be offering a high performance silicon photonics platform with TSMC. The new platform will enable system-on-chip integration of optical interconnect with CMOS logic and will be leverage TSMC’s 7nm CMOS technology. The Luxtera platform is targeting next-generation silicon photonics solutions to deliver 100G-per-lane optical interconnects, starting with 100GBase-DR and 400GBase-DR4 transceivers which they anticipate launching in 2018.

This was promptly followed by a press release from TowerJazz where they too announced they will be providing a new Silicon Photonics process targeting the Optical Transceiver Electronics market. The TowerJazz SiPho process will be based on their SiGe BiCMOS process. When you start seeing production foundries like TSMC, TowerJazz and GLOBAL FOUNDRIES (as announced late last year) getting into the market you know the significant volumes are on their way. This is big!

And lastly, of note was an offer was made by IDT to purchase GigPeak for $250M. GigPeak offers optical interfaces for communications, data centers and military and avionic modules. GigPeak had record profits for its fourth quarter and fiscal 2016 with shipments of its 40 Gbps QSFP+ and 100Gbps QSFP28 ICs for active optical cables (AOCs) and optical transceiver modules into data center customers. The company is also currently sampling driver and trans-impedance amplifier (TIA) ICs for 200 Gbit/s short-reach and long-reach PAM4 Ethernet applications.

The second week of my travels was spent on the east coast of the U.S. traveling up and down the I-90 corridor. One of the most interesting observations of that week was the uptake in the number of integrated photonics projects coming from commercial companies versus past activity which was primarily driven by universities and R&D labs. This was echoed by Twan Korthorst, CEO of PhoeniX Software, where he presented a graph at the PIC International Conference showing a shift of new PhoeniX users coming from commercial companies as opposed to academia. On a side note, PhoeniX’s OptoDesigner tool won the EPIC Award at this year’s PIC International show in the design and test category. While good news for PhoeniX, the interesting part for the reader is that more than 6000 engineers cast ballots for this award. That’s a lot of people for a nascent industry. It also explains PhoeniX’s 45% CAGR for PIC tools over the last four years.

The final week of my travels was spent in Boston at the MIT campus where I attended an AIM Photonics sponsored meetings to road map requirements for the integrated photonics ecosystem. In many cases members were excited to see road map items being accelerated forward by industry.

One of the most interesting presentations was given by Microsoft where they presented on their integrated photonics work used in Time-of-Flight (ToF) cameras and sensors. These cameras give full 3D imaging information for applications such as facial recognition security features. ToF sensors are already in the iPhone 7 and could be applied to future laptops, phones, TVs and gaming consoles. Cameras with 3D depth capabilities can be applied to a wide variety of applications such as gaming, in-air gesturing and augmented reality.

Microsoft, Apple, Intel and Google are all working to bring this ToF technology to bear. Now that would represent some real volume.

This is just the beginning as engineers are barely scratching the surface of what can be done with integrated photonics. From long haul telecommunications, RF and microwave applications, WIFI networks and data center switches, to high volume applications in automotive, mobile devices, industrial sensing and medical and bio-sensing arenas, it’s time to start placing your bets. Hang on to your hats. It’s going to be a wild ride for the next decade!

See also:
Luxtera and TSMC Collaborate on NexGen Silicon Photonics
TowerJazz Announces Silicon Photonics Offering
IDT Makes Offer on GigPeak
PhoeniX Software Selected for PIC Design & Test Award


When Will we Replace the 3.5 mm Jack in Modern Phones?

When Will we Replace the 3.5 mm Jack in Modern Phones?
by Eric Esteve on 04-05-2017 at 7:00 am

You have certainly experienced that modern mobile phones are used for more than phone calls and do not have room for multiple connectors. A new approach for audio connectivity is needed, allowing product designers to retire the 3.5mm jack. Considering the USB audio protocol to replace the analog audio solutions, typically using a 3.5mm phone jack, seems to be the obvious solution, but the legacy USB audio cannot replace the 3.5mm phone jack in mobile phones and other portable, battery powered products, as it would drain the battery too quickly.

The modern solution was announced on September 27, 2016 by the USB-IF, it’s the USB Audio Design Class (ADC) 3.0…

ADC 3.0 is expected to be the new standard for phones using the USB Type-C[SUP]TM[/SUP] connector, because, unlike legacy USB audio protocols, ADC 3.0 implementations is enabling significant system power savings, which are critical to low-power mobile applications.

Before describing how ADC 3.0 implementation enable power savings, let’s take a look at the power profiles for mobile phone with analog headset. For analog audio solutions, the communication between the source and the user is burst based, allowing functional blocks to enter lower power modes between bursts of activity. It’s a pretty efficient solution in term of power consumption, but it’s using the 3.5mm phone jack and the goal is to remove it.

Unlike analog headsets, USB audio headsets use isochronous transfers. Isochronous transfers provide the guaranteed bandwidth which is required for audio streams, but at the cost of a higher power consumption. To strongly reduce this power consumption, the USB ADC 3.0 is smartly using the new power state Link Power Management (LPM L1) for High Speed USB.

Isochronous transfers for legacy USB audio occurs every 1ms for Full Speed USB. The bus is idle between transfers, but cannot enter Suspend L2. The combination of High Speed USB bursting and LPM L1 Suspend offers significant power savings opportunities. The next figure shows the power saving events of the new audio specification as a function of time.

Integrating the USB 2.0 LPM L1 specifications seems to be a good option to reduce the power consumption in such a way that USB ADC 3.0 could replace analog audio solution, and the USB Type-C connector replace the 3.5 mm jack. But a proof of concept was required to allow the ADC 3.0 specification to progress. Synopsys has demonstrated the successful proof of concept using Synopsys DesignWare® USB IP.
I suggest you to read this articlewritten by Morten Christiansen, Technical Marketing Manager, Synopsys, and I recommend you spend some time on the Figure 4: “Proof of concept for LPM L1 power save”. You will go deep inside the packet based USB protocol and understand how LPM L1 really works. In this system used for the proof of concept, the LPM L1 Suspend residency is 87.8% (3.513ms of a 4ms service interval).

The author reminds that PHY power consumption is important to consider alone, as it is typically much higher than controller power consumption. LPM L1 power is typically less than 1% of active or idle power and using LPM L1 can result in PHY power savings of 86%. And the conclusion is that USB Audio Device Class 3.0 USB headset solutions that are power-competitive with legacy analog headset solutions.

To implement ADC 3.0 in low-power products like phones, tablets or laptops, designers will need an ADC 3.0 compliant host controller with Hardware Controlled Link Power Management capability. The ADC 3.0 compliant device controller implementation will require some important changes and designs supporting ADC 3.0 require LPM L1 capable PHYs. Synopsys is claiming that lead customers are already designing ADC 3.0 compliant headsets using industry leading LPM-capable USB IP.
As an end user, we should have the opportunity to buy smartphones equipped with a single connector in the near future. USB Type-C specification allowing to support Power Delivery, Authentication, Audio (ADC 3.0) is expected to see large adoption as 2 billion+ units are forecasted by IHS to ship in 2019, or 40% of total units.

The article from Synopsys about USB Type-C ADC 3.0:
https://www.synopsys.com/designware-ip/newsletters/technical-bulletin/usb-audio-dwtb-q117.html
By Eric Esteve from IPnest


When Once is Not Enough, But Unlimited is Too Much

When Once is Not Enough, But Unlimited is Too Much
by Tom Simon on 04-04-2017 at 12:00 pm

When people think about non volatile memory, the first thing that usually comes to mind is NAND flash like that used in SSD’s or in microcontrollers to hold on-board code. Of course, there is also EEPROM and other types of NVM as well that can be used to hold data and code for the multitude of connected devices that are so common now. For many SOC designs NAND flash or EEPROM might be the go-to technology for re-writeable persistent storage. However, there are some down sides to this technology, and there is an attractive alternative worth considering. So, what might make SOC designers think twice about going with NAND flash?

I learned more about the specifics of this while I was at the TSMC Technology Symposium on March 15[SUP]th[/SUP]. I had the opportunity to have lunch with Ken Wagner, Sr. VP of Engineering, and Andrew Faulkner, Sr. Director of Marketing, at Sidense, provider of one time programmable (OTP) IP for integration into SOC’s.

The top drawback for considering adding NAND flash or many other NVM’s is that they require additional mask layers or special processes. These requirements cost more money, introduce risk and can limit options for fabrication. In many applications security and durability are essential. Technologies that use charge storage can be perturbed and suffer data loss. They are also vulnerable to thermal degradation. IoT devices and mobile devices can be subjected to intense environmental conditions. Another application where heat is a significant factor is in the automotive arena. Floating charge devices can be probed externally to reveal their contents. Side channel attacks can also sometimes decipher the contents of flash storage.

Nevertheless for frequent re-writes, flash memory is a good NVM option. Frequently in addition, SOC’s often include one time programmable (OTP) memory for storing parameters, data, or even code that do not change. If rare changes are needed, OTP can be configured as few-times programmable (FTP) so that field updates can be performed as a necessity.

As middle ground, Sidense offers what they call emulated multiple time programmable (eMTP) NVM. Sidense has cleverly implemented features in their RTL NVM controller, which is delivered as RTL, to automatically handle the remapping of the one time writable physical layer, into an emulated multiple write interface. The granularity of the re-write size and number of re-writes available are configured during SOC implementation.

Once the restrictions of extremely limited re-writes is lifted, the technology used to implement anti-fuse NVM starts to look extremely interesting. No extra mask layers are needed. It can be implemented on conventional planar MOS transistors or on FinFET’s. So, the range of available nodes is very large, right up to the latest TSMC offerings. The bits are physically encoded by irreversibly breaking down the gate oxide with tiny conductive holes. One benefit of this approach is that there is no method to physically observe the state of a bit cell due to the virtually atomic level of the write action.

Another advantage of the 1T-NVM bit cell is that it is extremely reliable. Sidense offers TDDB testing that assures 10-year memory retention of 100%. Their NVM is qualified for AEC-100Q Grade 0 and 1 automotive applications. Sidense’s 1T-NVM bit cell is very compact so there are area savings with this approach. An available on-board charge pump can eliminate the need for external power pins for the write operation. Write times are very short, offering another advantage over other technologies. The arrays they offer are large enough to give very high re-write counts for data and parameters, and numerous re-writes for code storage.

While, eMTP NVM is not suitable for all applications, it certainly offers an attractive solution in between OTP NVM and the alternative of having to change over to NAND flash with all the consequences. Sidense has much more detailed information available about emulated multiple times programmable non volatile memory on their website.


One Cellular Technology to Rule Them All

One Cellular Technology to Rule Them All
by Bernard Murphy on 04-04-2017 at 7:00 am

5G, the planned successor to earlier mobile network standards, holds all kinds of promise for new capabilities beyond LTE, but for a while seemed stuck in debate on exactly what the standard should cover. Several problems are apparent. A path to higher bit-rates is complicated because of spectrum shortage and fragmentation (plans are apparently underway to ameliorate this problem), support is required for a wide range of applications with very dissimilar needs and the IoT requires support for massive numbers of devices, growing exponentially beyond traditional cellular demand.


This wide scope implies an even wider range of capabilities, particularly at base stations/cells. Massive IoT will expect to work at low data-rates per device but with very high connection densities (many devices within a limited area). Enhanced mobile broadband (eMBB) needs support for extremely high data rates to support 4K screens and AR/VR for example. Meanwhile mission-critical applications, in ADAS for automotive, medical devices and industrial uses can operate only with high expectations of reliability and low-latency.

Verizon with their V5GTF consortium had already been developing an early version of the standard but apparently not fast enough (or maybe not independently enough?) for others. An impressive group of telcos, chip and equipment providers announced on the first day of Mobile World Congress this year that they were promoting their own early version in 5G NR. Since this is ahead of a finalized spec (slated for 2020), solutions developed at this stage will need to be flexible to adjust to intermediate milestones in the run-up to the final release, but either way we may be seeing solutions earlier than originally expected. What once may have been relatively relaxed schedules to design products for this space may become more of a scramble.

Why is this a big deal? Because 5G-NR support, particularly in macro cells and small cells, is significantly more challenging than for LTE. As understood today, this aggregates simultaneous use of LTE, LTE-A Pro, 5G NR, WiFi 11ax/ad and WiGig in a unified protocol (an evolutionary rather than revolutionary approach, providing backward compatibility with those standards.) It must limit round-trip latency to 1ms or 0.5ms for ultra-low latency applications. It must support massive MIMO (256+ antennas) and multi-user MIMO, and in order to support high-density UEs/edge nodes (as much as a million per km[SUP]2[/SUP]) it must handle advanced beamforming.

This is already incredibly challenging – processing multi-user, multi-protocol, multi-IO connectivity through many antennas with exceptionally low latency (and low power), while computing and juggling complex beamforming strategies to optimize communication in a dense network. To maintain flexibility both in protocol handling and adapting to spec evolution, this will require SDR (software-defined radio) strategies, so significant software is needed to build out a solution, but high rates and low latencies require hardware implementation wherever possible. And on top of that, the standard isn’t yet finalized so designers know whatever they build today must inevitably evolve. This doesn’t look like a game for the faint of heart, but then again waiting for the standard to freeze before you jump into the game doesn’t look like a winning strategy either.


That’s where the CEVA XC-12 cluster architecture comes in, shown in the CEVA reference design above. We’re used to clustered CPUs; in fact, clustered DSPs are not a new idea but they make perfect sense in this context where massive parallel processing is absolutely essential. CEVA states that the XC12 has been designed from the ground-up for this domain, able to operate at 1.8GHz in 10nm processes, supporting massive computation through QUAD vector processing engines and up to 256×256 matrix processing. Each 5G-NR carrier is processed using a single cluster (4 XC12 cores). To squeeze latency to a minimum, each XC12 pair within a cluster is connected by fast interconnect busses and can share memory, allowing the pair to share task workloads such as channel estimation or data symbol processing.

The reference design is completed through other components from CEVA – L2 cache, X2 for scheduling and control, hardware accelerators including FFT/IFFT and forward error control encode and decode, and beamforming, each implemented in hardware for maximum performance. CEVA also provides optimized 3G, 4G and 5G libraries for all Physical Layer control and data channels so an OEM can build a complete PHY much faster. And they provide Drivers and Libraries running on X2 and XC12 to control the HW Accelerators included in the reference design.

This looks like a pretty good start to build out a 5G-NR-capable SoC. You can also be confident that you won’t be the first provider heading down this path. CEVA has already signed deals for a 5G base-station DSP with one OEM and a 5G UE modem DSP with another OEM targeting the Winter Olympics in South Korea. Click HERE to watch the Webinar on CEVA’s solution for 5G-NR.

More articles by Bernard…


Shootout at 22nm!

Shootout at 22nm!
by Scotten Jones on 04-03-2017 at 4:00 pm

For an industry that drives improvement at an exponential rate it is funny how often something old is new again. Intel went into high volume production on 22nm in 2011, and TSMC and Samsung have both had 20nm technologies in production for several years. And yet, recently we have seen renewed interest in 22nm. GLOBALFOUNDRIES has a 22nm FDSOI technology (22FDX) ramping now, at their recent technology forum TSMC announced a bulk 22nm technology 22ULP for 2017 production and this week Intel also announced a new 22nm FinFET technology 22FFL or 2017 production. Why the resurgence of interest in 22nm?

20nm/22nm is the last node where the industry primarily relied on planar bulk technology and is also the break point where multi-patterning starts to come in. At smaller dimensions’ leakage issues have driven the transition to fully depleted devices with FinFETs leading the way. FinFETs provide excellent drive current and a good scaling path with 16nm/14nm processes in high volume production, 10nm ramping and 7nm on the horizon. With each new node, the cost to manufacture a transistor has come down and Moore’s law has continued, but this has come at a high price. Design rules have been growing rapidly and the cost to design on these processes is so high that only the largest volume products can justify the necessary investment in development costs. With each new node, fewer products will be designed onto each new technology. One of the key emerging areas for the industry is IOT where the opportunity space is expected to be split between many lower volume products. The need for processes with lower design costs is obvious. IOT we will also need very low power, RF, analog and reasonable digital density. All of the three companies mentioned above are targeting this market with these new 22nm processes.

The key 22nm process objectives are low design cost, little or no multi-patterning, low power and the features needed for IOT and mobile.

GLOBALFOUNDRIES (GF) 22FDX
I have written about 22FDX previously here and here. 22FDX is based on fully depleted SOI (FDSOI) and offers the unique ability to use biasing to achieve multiple threshold voltages and dynamically scale performance. 22FDX offers the lowest operating voltage of any process I am aware of at 0.4 volts. Since power consumption is proportional to voltage squared, 22FDX should provide very low power operation. 22FDX offers 50% faster speed or 18% lower power than GLOBALFOUNDRIES 28nm process. 22FDX also offers excellent RF performance with NMOS FT/FMAX of 350/325GHz and PMOS FT/FMAX of 290/250GHz. This is likely far higher than either of the other 22nm processes, for example GF 14LPP FinFET process only achieves FMAX of ~150GHz. 22FDX is ramping up now.

TSMC 22ULP
Announced only a few weeks ago, 22ULP is due to ramp by the end of 2017. 22ULP is based on a bulk technology and TSMC claims the Ion/Ioff curve is identical to a “22nm FDSOI” technology. The operating voltage is 0.6 volts and the process is said to offer 15% higher performance or 35% lower leakage than TSMC’s 28nm process.

Intel 22FFL
Announced this week, 22FFL is due to ramp in Q4 of 2017. 22FFL is based on Intel’s 22nm FinFET process that has been in production since 2011. 22FFL offers HP transistor with performance similar to Intel’s 14++ process and low leakage transistors with >100x lower leakage, see figure 1. RF is supported although FinFET RF performance is likely not nearly as good as FDSOI due to higher parasitic capacitances.


Figure 1. 22FFL Performance.

Process comparison
All three company’s processes are targeted at lower design costs and meeting the needs of IOT types of applications. Table 1 compares some of the key process characteristics for the three processes.

border=”1″ align=”center”
|-
| style=”width: 156px” | Company
| style=”width: 65px; text-align: center” | GF
| style=”width: 66px; text-align: center” | TSMC
| style=”width: 60px; text-align: center” | Intel
|-
| style=”width: 156px” | Process name
| style=”width: 65px; text-align: center” | 22FDX
| style=”width: 66px; text-align: center” | 22ULP
| style=”width: 60px; text-align: center” | 22FFL
|-
| style=”width: 156px” | Process type
| style=”width: 65px; text-align: center” | FDSOI
| style=”width: 66px; text-align: center” | Bulk
| style=”width: 60px; text-align: center” | FinFET
|-
| style=”width: 156px” | CPP (nm)
| style=”width: 65px; text-align: center” | 90 est
| style=”width: 66px; text-align: center” | 105 est
| style=”width: 60px; text-align: center” | 108
|-
| style=”width: 156px” | MMP (nm)
| style=”width: 65px; text-align: center” | 78 est
| style=”width: 66px; text-align: center” | 80 est
| style=”width: 60px; text-align: center” | 90
|-
| style=”width: 156px” | Tracks
| style=”width: 65px; text-align: center” | 8
| style=”width: 66px; text-align: center” | 7
| style=”width: 60px; text-align: center” | 7
|-
| style=”width: 156px” | CPP x MMP x Track (nm2)
| style=”width: 65px; text-align: center” | 56,160
| style=”width: 66px; text-align: center” | 58,800
| style=”width: 60px; text-align: center” | 68,040
|-
| style=”width: 156px” | Vdd (volts)
| style=”width: 65px; text-align: center” | 0.4
| style=”width: 66px; text-align: center” | 0.6
| style=”width: 60px; text-align: center” | NA
|-

Table 1. Process comparison

Looking at the three processes the GF 22FDX process appears to likely have the densest logic (although we do not have exact numbers for GF and TSMC). 22FDX use 2 multi-patterning layers in the middle of line to drive tighter interconnect plus has the smallest projected contacted poly pitch. 22FDX will also likely have the best RF performance and active power consumption. On other performance and leakage metrics we don’t have enough data to draw any conclusions yet.

One critical factor in designing for any technology is the availability of IP. With TSMC’s 22ULP being a planar bulk shrink from 28nm they will likely have the richest IP offering providing the fastest deign path. At their manufacturing day Intel had a foundry panel including executives from Cadence, ARM and Synopsys and they are clearly working on that area but likely still playing catch up. I know GF is also putting a lot of emphasis on IP and the design environment but it isn’t clear to me how much traction they are getting.

Conclusion
Designers of IOT and other mobile devices now have three new 22nm processes to choose from. The three processes take very different approaches. GF’s 22FDX is the most radical departure from the main stream but also likely delivers the best density, RF and power performance. TSMC’s 22ULP bulk planar process can likely offer the richest IP environment. Intel’s 22FFL is an evolution of the 22GP process that is one of the highest yielding processes in Intel’s history and offers 14++ like performance with very low leakage transistors.


Apple Going (IP) Vertical with no Imagination!

Apple Going (IP) Vertical with no Imagination!
by Eric Esteve on 04-03-2017 at 12:00 pm

What conclusion could we derive from the recent (April 3) PR from Imagination where we learn that the company “has been notified by Apple Inc. (“Apple”), its largest customer, that Apple is of a view that it will no longer use the Group’s intellectual property in its new products in 15 months to two years time, and as such will not be eligible for royalty payments under the current license and royalty agreement.”?



Apple A9 Application Processor

The first very basic lesson is “good business practices”: it’s always dangerous for a company to rely on a single customer for more than 50% of the revenues. The royalties paid by Apple to Imagination in fiscal year 2015 (ending in April 2016) was US$ 89.7, for a total revenue of US$ 177. If you consider the GPU IP only (without the MIPS CPU royalty revenues of US$ 34.7), Apple royalty payment represent 78% of Imagination royalty revenue linked with the company flagship product… (all figures in $ million).

The second lesson is “differentiation”: Apple is continuing to deploy a strategy based on the internal development of the essential application processor functions. Since the acquisition of PA Semi in 2008, Apple develops an ARM compliant CPU (under an architecture license with ARM). By the way, the internal development of this CPU has allowed Apple to be the first to launch a smartphone based on a 64-bit CPU application processor. If internal IP development allows such kind of differentiation, that’s a win!
Just to comfort this second lesson, just take a look at Qualcomm: the fabless chip maker has been, and is still the WW #1 application processor vendor. If you look at Snapdragon architecture, you will see:

·ARM 64-b compliant CPU (architecture license)
·ADRENO GPU (internal design)
·Hexagon DSP (internal design)

And, if you remember the last time that Qualcomm has integrated standard ARM Cortex 64-bit CPU, it was to react to Apple launch of 64-bit CPU while the internal 64-bit CPU was not yet available, and the result was a Snapdragon product showing such a high-power consumption that it was almost not usable by system manufacturers…

The big lesson here is that if you want to design the highest differentiated product, you need to invest enough to develop the main IP (CPU, GPU or DSP) internally, the real lesson being that the leaders, system manufacturer (Apple) or chip maker (Qualcomm), did it and it was a strike!

When I read the PR from Imagination, I am afraid that the next lesson will be “Be prepared to pay a fortune to lawyers, and this for years”. In fact, Imagination is preparing to fight on the legal field:

“Imagination believes that it would be extremely challenging to design a brand new GPU architecture from basics without infringing its intellectual property rights, accordingly Imagination does not accept Apple’s assertions”. This may be a way to put pressure on Apple in order to push them to pay a license, even if ARM is no more using Imagination GPU… I am not a legal expert and not a GPU architecture expert too, so I can’t really comment.

Last point (and maybe a business lesson): I can comment about imagination strategy during the last 5 or 6 years. When you look at their annual report, that I did as recently on Saturday (but it was a coincidence), you can see that, on top of the GPU and CPU IP businesses, Imagination is listing 8 to 10 other businesses or product line (like Ensigma IP). When you are the GPU IP #1 vendor like Imagination was, it’s really better to keep focus and stay #1, instead of diversifying into products of low synergy, if any.

By Eric Esteve from IPnest


CEO Interview: Sanjay Keswani of Consensia

CEO Interview: Sanjay Keswani of Consensia
by Daniel Nenni on 04-03-2017 at 7:00 am

Sanjay Keswani founded Consensia in 2013. He has deep experience in the high-tech industry, guiding some of the world’s high profile technology brands through complex innovation and business transformation projects including companies such as Atmel, KLA-Tencor, Hughes Aircraft, and Motorola Mobility. Consensia customers include ARM, NXP, Qualcomm, Broadcom, Intel, Microsemi, and Cavium.

Who is Consensia? What is your vision for the company?
Consensia is a Dassault Systèmes channel partner, focused on the semiconductor, high tech (i.e. electronic systems / connected devices) and medical device industries. We provide software based solutions for efficient product development, in addition to consulting & training services, and technical support for the entire range of Dassault products . These include design productivity, semiconductor IP and product lifecycle management.

With significant M&A activity in the semi and systems markets, we see more vertical integration; so companies that were selling a few IC products to integrators and systems companies are now selling a diverse range of ICs, board based products and complete systems. We take a systems engineering view from IC to complete systems and enable companies to manage the product development process at all levels of their product. Taking a holistic view of product development is very critical to delivering high quality products to market. We are uniquely placed to offer a wide range of solutions to our customers.

Our vision is to enable our customers to serve a variety of market segments by enabling them to adhere to processes needed by their customers. For example, a semiconductor company selling into the automotive market has to adhere to APQP, and PPAP requirements along with DFMEAs and PFMEAs. Companies are also subject to increased level of compliance like RoHS, WEEE, REACH, and Conflict Minerals. Our solutions enable compliance activities on the same innovation platform.

What are your key offerings for the semi industry?
Our solutions connect design engineering, product engineering, and operations engineering in the semiconductor world. We provide solutions for both domains – design and operations.

In the design domain, (Synchronicity) DesignSync is a product that has been around for over 15 years. It’s been publicly disparaged by our competitors, but it’s interesting that all the ‘new’ DDM features that have been released by competitors over the last 2-3 years – local read/write caching, virtual workspaces, change based or manifest version control – have been part of DesignSync for as much as 10 years ago. There are significant updates to it ever year, so this year we are going to be running a series of ‘What’s New Din DesignSync’ webinars to show off what it’s capable of. DesignSync is the most widely used DDM solution in the world in terms of number of users and Dassault Systemes continues to invest heavily in new capabilities based on customer feedback.

Pinpoint is a unique analytics solution that enables ASIC/SOC & FPGA design teams to get a more accurate picture of their design progress. For example, if you have 100 RTL blocks and you’ve frozen 90 it doesn’t necessarily mean your design is 90% complete. Pinpoint helps bring information from multiple data sources – STA, Power, Physical Verification – into a single dashboard so that you can see how many failing timing paths you have at a certain clock speed, how many of a certain type of DRC you have, etc. so you have a much more realistic real-time and historic trend line view of progress, so you know exactly how mature your design is and when you are likely to reach design closure.

Issue and Defect Management is part of our IP Management – something that our semi customers use to help design teams track, trace and address post manufacturing issues that arise in the field. This is critical for semiconductor companies serving the automotive market where traceability is mandatory

On the operations side, we have the only ‘Out of the Box’ solution for managing the configuration of a chip – from wafer to finished goods. Our solution has the ability to capture product and customer requirements and create only valid and available product configurations. As a chip moves through its process lifecycle (Fab->Sort ->Assembly->Package->Test->Mark->Finished Good), the IC BOM grows pulling together parts and information from many different processes and their locations. We manage the entire configuration along with product costing.

We offer a comprehensive set of quality processes like CAPA, Non-Conformance, Audits which are of value to an operations executive. They use the same innovation platform as the other processes we enable.

What is your differentiation in terms of solution offerings and value proposition?
The difference between us and our competitors is that (i) we take a platform based approach to IP Management and (ii) we’ve been in the design data management business for a long time (since Synchronicity).

Taking a platform based approach to solving the complete product lifecycle challenges of our semiconductor customers means we work with them on how best to optimize design productivity, configuration (design data and manufacturing configuration) management, management of internal and external IP and how to increase the asset value of the company by using best practices to drive efficiencies in their product development processes

Several of our competitors focus on DDM – and some have recently (last 12 months) added IP Management. Dassault Systemes and Consensia have been in the IP management business for many years. We are not a “one trick” pony. While DDM is a critical part of design infrastructure, there are many other processes which are of strategic value which our competition does not address.

As far as IP Management is concerned every customer manage their IP differently; they have different business rules and operating models – there isn’t a one size fits all out of the box. So we take a consultative approach to designing a system that fits their MO AND allows them to understand and monetize their IP assets by reducing NRE, increasing re-use and maximizing their use of externally licensed IP.

The design data management solution is just one component in a bigger IP/SOC management picture that involves IP security, compliance (ITAR, 3[SUP]rd[/SUP] party IP tracking and tracing), role based access, issue defect and management – and many more components that are active long after an SOC has been released.

We come at it from a ‘top down’, pragmatic approach with IP Management – so DesignSync becomes the ‘hub’ that manages other DDM repositories, like SVN or Perforce, which become just another repository from which to pull or publish BOMs.

We engage with companies at an early stage and they can grow with Consensia’s support and solutions to a billion dollar revenue company. Our solutions are modular; we can start with virtually any process. Our engagement model is very flexible and our objective is to have a close relationship with our customers to enable their profitable growth.

Innovation and collaboration are overused terms; how do you specifically address those areas?
All of our solutions are designed for collaboration – we bring innovation at multiple levels. PLM solutions are addressing at a project governance level for global teams; whereas DesignSync enables individual IC design team members to collaborate by automating the complete lifecycle of an SOC from management of individual IP components to the automated SOC release management process.

Also read: Webinar: Top Five Challenges Preventing Design Closure!


Everything a Designer Wants to Ask About FDSOI

Everything a Designer Wants to Ask About FDSOI
by Adele Hars on 04-01-2017 at 12:00 pm

So you’ve got questions about FD-SOI? For chip designers in Silicon Valley, there’s a great opportunity to get answers from some of the world’s leading design experts. It’s coming up fast: April 14th, the SOI Consortium is organizing a full day of FDSOI tutorials for chip designers. Bear in mind that it’s not a sales day. It’s a learning day.

The courses will be given by top professors at top universities (including UC Berkeley, Stanford, U. Toronto and Lund). They’ve all also spent years working closely with industry, so they really understand the challenges designers face. They’ve helped design real chips, and have stories to tell.

Each one will address FD-SOI specific design techniques for: analog and RF integration (millimeter wave to high-speed wireline), ultra-low-power memories and microprocessor architecture, and finally energy-efficient digital and analog-mixed signal processing designs.

The tutorial day will be held in San Jose, beginning at 8am and running until 3pm. Each professor’s course will last one hour. Click here for registration information.

(BTW, the Tutorial Day follows the day after the annual SOI Silicon Valley Symposium in Santa Clara, which will be held on April 13th.)

Here’s a quick preview at what each professor will be addressing during the FDSOI Tutorial Day.

FDSOI Short Overview and Advantages for Analog, RF and mmW Design
– Andreia Cathelin, Fellow, STMicroelectronics, France

Professor Cathelin (yes, in this case she’s an ST Fellow, but she’s also a professor) has deep experience designing ground-breaking chips.


Summary slide from Professor Andreia Cathelin’s course at the upcoming FDSOI Tutorial(Courtesy: SOI Consortium and ST)


She’ll start with a short overview of basic FDSOI design techniques and models, as well as the major analog and RF technology features of 28nm FDSOI technology. Then the focus shifts to the benefits of FD-SOI technology for analog/RF and millimeter-wave circuits, considering the full advantages of wide-voltage range tuning through body biasing. For each category of circuits (analog/RF and mmW), she’ll show concrete design examples such as an analog low-pass filter and a 60GHz Power Amplifier (an FDSOI-aware evolution of the one featured on the cover of Sedra/Smith’s Microelectronics Circuits7thedition, which is probably on your bookshelf.) These will highlight the main design features specific to FD-SOI and offer silicon-proof of the resulting performance.


Unique Circuit Topologies and Back-gate Biasing Scheme for RF, Millimeter Wave and Broadband Circuit Design in FDSOI Technologies– Sorin Voinigescu, Professor, University of Toronto, Canada.

Professor Voinigescu is particularly well-known for his work in millimeter wave and high-speed wireline design and modeling (which are central to IoT and 5G). He’s worked with SOI-based technologies for over a decade. His course will cover how to efficiently use key features of FD-SOI CMOS technology in RF, mmW and broadband fiber-optic SoCs. He’ll first give an overview at the transistor level, presenting the impact of the back-gate bias on the measured I-V, transconductance, fT and fMAX characteristics. The maximum available power gain (MAG) of FDSOI MOSFETs will be compared with planar bulk CMOS and SiGe BiCMOS transistors through measurements up to 325 GHz.

Summary slide from Professor Sorin Voinigescu’s course at the upcoming FDSOI Tutorial (Courtesy: SOI Consortium and U. Toronto)


Next, he’ll provide design examples including LNA, mixer, switches, CML logic and PA circuit topologies and layouts that make efficient use of the back-gate bias to overcome the limitations associated with the low breakdown voltage of sub-28nm CMOS technologies. Finally, he’ll look at a 60Gb/s large swing driver in 28nm FDSOI CMOS for a large extinction-ratio 44Gb/s SiPh MZM 3D-integrated module, as a practical demonstration of the unique capabilities of FDSOI technologies that cannot be realized in FinFET or planar bulk CMOS.


Design Strategies for ULV memories in 28nm FDS-SOI
– Joachim Rodrigues, Professor, Lund University, Sweden

Having started his career as a digital ASIC process lead in the mobile group at Ericsson, Professor Rodrigues has a deep understanding of ultra-low power requirements. His tutorial will examine two different design strategies for ultra-low voltage (ULV) memories in 28nm FD-SOI.

For small storage capacities (below 4kb), he’ll cover the design of standard-cell based memories (SCM), which is based on a custom latch. Trade-offs for area cost, leakage power, access time, and access energy will be examined using different read logic styles. He’ll show how the full custom latch is seamlessly integrated in an RTL-GDSII design flow.


Summary slide from Professor Joachim Rodrigues’ course at the upcoming FDSOI Tutorial (Courtesy: SOI Consortium and Lund U.)


Next, he’ll cover the characteristics of a 28nm FD-SOI 128 kb ULV SRAM, based on a 7T bitcell with a single bitline. He’ll explain how the overall energy efficiency is enhanced by optimizations on all abstraction levels, from bitcell to macro integration. Degraded performance and reliability due to ULV operation is recovered by selectively overdriving the bitline and wordline with a new single-cycle charge-pump. A dedicated sense-amplifierless read architecture with a new address-decoding scheme delivers 90MHz read speed at 300mV, dissipating 8.4 fJ/bit-access. All performance data is silicon-proven.

Energy-Efficient Processors in 28nm FDSOI– Bora Nikolic, Professor, UC Berkeley, USA

Considered by his students at Berkeley as an “awesome” teacher, Professor Nikolic’s research activities include digital, analog and RF integrated circuit design and communications and signal processing systems. An expert in body-biasing, he’s now working on his 8th generation of energy-efficient SOCs. During the FDSOI tutorial, he’ll cover techniques specific to FDSOI design in detail, and present the design of a series of energy-efficient microprocessors. They are based on an open and free Berkeley RISC-V architecture and implement several techniques for operation in a very wide voltage range utilizing 28nm FDSOI. To enable agile dynamic voltage and frequency scaling with high energy efficiency, the designs feature an integrated switched-capacitor DC-DC converter. A custom-designed SRAM-based cache operates in a wide 0.45-1V supply range. Techniques that enable low-voltage SRAM operation include 8T cells, assist techniques and differential read.



Summary slide from Professor Bora Nikolic’s course at the upcoming FDSOI Tutorial (Courtesy: SOI Consortium and UC Berkeley)


Pushing the Envelope in Mixed-Signal Design Using FD-SOI
– Boris Murmann, Professor, Stanford University, USA

If you’ve ever attended a talk by Professor Murmann, you know that he’s a really compelling speaker. His research interests are in the area of mixed-signal integrated circuit design, with special emphasis on data converters and sensor interfaces. In this course, he’ll look at how FD-SOI technology blends high integration density with outstanding analog device performance. In same-generation comparisons with bulk, he’ll review the specific advantages that FD-SOI brings to the design of mixed-signal blocks such as data converters and switched-capacitor blocks. Following the review of such general benchmarking data, he’ll show concrete design examples including an ultrasound interface circuit, a mixed-signal compute block, and a mixer-first RF front-end.

Summary slide from Professor Boris Murmann’s course at the upcoming FDSOI Tutorial (Courtesy: SOI Consortium and Stanford U.)



Key Info About the FD-SOI Tutorial Day

Event
:Designing with FD-SOI Technologies
Where: Samsung Semiconductor’s Auditorium “Palace”, San Jose, CA
When: April 14th, 2017, 8am to 3pm
Cost: $475
Organizer: SOI Industry Consortium
Pre-registration required – click here to sign up on the SOI Consortium website.