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Computational Imaging Craves System-Level Design and Simulation Tools to Leverage AI in Embedded Vision

Computational Imaging Craves System-Level Design and Simulation Tools to Leverage AI in Embedded Vision
by Kalar Rajendiran on 07-03-2023 at 10:00 am

Typical Pipelines

Aberration-free optics are bulky and expensive. Thanks to high-performance AI-enabled processors and GPUs with abundant processing capabilities, image quality nowadays relies more on high computing power tied to miniaturized optics and sensors. Computational imaging is the new trend in imaging and relies on the fusion of computational techniques and traditional imaging to improve image acquisition, processing and visualization. This trend has become increasingly important with the rise of smartphone cameras and involves the use of algorithms, software and hardware components to capture, manipulate and analyze images. It results in improved image quality and enhanced visual information and additionally enables meaningful data extraction which is critical for embedded vision applications.

While there are several advantages from computational imaging, there are many challenges that need to be addressed to enjoy the full potential. The design and simulation tools used by optical designers, electronics engineers, and AI software engineers are often specialized for their respective domains. This creates siloes, hindering collaboration and integration across the entire imaging pipeline and results in suboptimal system performance.

A system-level design and simulation approach that considers the entire imaging system would optimize image quality, system functionality and performance (cost, size, power consumption, latency…). It would require integrating optical design, image sensor and processor design, image processing algorithms and AI models. Synopsys recently published a whitepaper that discusses how the gaps in computational imaging design and simulation pipelines can only be overcome with system-level solutions.

Leveraging AI Algorithms to Improve Computational Imaging Pipeline

Image Signal Processors (ISPs) process raw data from image sensors and perform various tasks to enhance image quality. Traditional ISPs are designed for specific functions and are hardwired for cost efficiency, limiting their flexibility and adaptability to different sensor classes. AI-based image processing utilizing neural networks (NN) shows promise in supplementing or replacing traditional ISPs for improving image quality.

Supplement or Replace Traditional ISPs

For example, a noise filter used in ISPs can enhance image quality but may discard crucial information present in the raw data. By analyzing chromatic aberration effects before digital signal processing (DSP), depth data contained in the raw sensor data can be indirectly extracted. This depth data can then be utilized by AI-based algorithms to reconstruct a 3D representation of a scene from a 2D image, which is not possible with current ISPs. In cases where the primary objective is for computer vision functions to interpret image content using machine learning rather than enhancing perceived quality for human viewing, working with raw data becomes advantageous. Utilizing raw data allows for more accurate object classification, object detection, scene segmentation, and other complex image analyses. In such cases, the presence of an ISP designed for image quality becomes unnecessary.

New Possibilities for Digital Imaging Systems

NNs excel in tasks such as denoising and demosaicing, surpassing the capabilities of traditional ISPs. They can also support more complex features like low-light enhancement, blur reduction, Bokeh blur effect, high dynamic range (HDR), and wide dynamic range. By embedding knowledge of what a good image should look like, NNs can generate higher resolution images. Combining denoising and demosaicing into an integrated process further enhances image quality. Additionally, NN-based demosaicing enables the use of different pixel layouts beyond the conventional Bayer layout, opening up new possibilities for digital imaging systems.

Cheaper Lenses Provide More Accurate Object Detection

NNs can produce better results for certain tasks, such as object detection and depth map estimation, when processing images captured by “imperfect” lenses. As an example, the presence of chromatic aberrations caused by imperfect lenses adds additional information to the image, which can assist the NN in identifying objects and estimating depth.

Co-designing Lens Optics with AI-based Reconstruction Algorithms

While smartphone-based ultra-miniaturized cameras have eclipsed Digital Single Lens Reflex (DSLR) cameras in the market, they face the limits of optics. Researchers at Princeton have explored the use of metalenses, which are thin, flat surfaces that can replace bulky curved lenses in compact imaging applications. They co-designed a metalens with an AI algorithm that corrects aberrations, achieving high-quality imaging with a wide field of view.

The key aspect of this co-design is the combination of a differentiable meta-optical image formation model and a novel deconvolution algorithm leveraging AI. These models are integrated into an end-to-end model, allowing joint optimization across the entire imaging pipeline to improve image quality.

Synopsys Solutions for Designing Imaging Systems

Synopsys offers tools to address the requirements of the entire computational imaging system pipeline. Its optical design and analysis tools include CODE V, LightTools, and RSoft Photonic Device Tools for modeling and optimizing optical systems. The company’s Technology Computer-Aided Design (TCAD) offers a comprehensive suite of products for process and device simulation as well as for managing simulation tasks and results.

Synopsys also offers a wide range of IP components and development tools to design and evaluate the ISP and computer vision (CV) blocks. These IP components include the MIPI interface, the ARC® VPX family of vector DSPs, and the ARC VPX family of Neural Processing Units (NPUs).

Synopsys ARC MetaWare MX Toolkit provides a common software development tool chain and includes MetaWare Neural Network SDK and MetaWare Virtual Platforms SDK. The Neural Network SDK automatically compiles and optimizes NN models while the Virtual Platforms SDK can be used for virtual prototyping.

Synopsys Platform Architect ™ provides architects and system designers with SystemC™  TLM-based tools and efficient methods for early analysis and optimization of multicore SoC architectures.

Summary

Computational imaging relies more than ever on high computing power tied to miniaturized optics and sensors rather than standalone and bulky but aberration-free optics. Promising system co-design and co-optimization approaches can help unleash the full potential of computational imaging systems by decreasing hardware complexity while keeping computing requirements at a reasonable level.

Synopsys offers design tools for the entire computational imaging pipeline spanning all domains from assisted driving systems in automotive, computer vision-based robots for smart manufacturing or high-quality images for mixed reality.

To access the whitepaper, click here. For more information, contact Synopsys.

Also Read:

Is Your RTL and Netlist Ready for DFT?

Synopsys Expands Agreement with Samsung Foundry to Increase IP Footprint

Requirements for Multi-Die System Success


A preview of Weebit Nano at DAC – with commentary from ChatGPT

A preview of Weebit Nano at DAC – with commentary from ChatGPT
by Daniel Nenni on 07-03-2023 at 6:00 am

Weebit Amir Regev Demo Screenshot
Weebit VP of Technology Development Amir Regev

Weebit Nano, a provider of advanced non-volatile memory (NVM) IP, will be exhibiting at the Design Automation Conference (DAC) this month. As part of this briefing I shared some of the basic the details with ChatGPT to see how it would phrase things. Here is some of what it suggested: “You won’t want to miss out on the epic experience awaiting you at our booth. It’s going to be a wild ride filled with mind-blowing tech and captivating demonstrations that will leave you in awe!”

ChatGPT is still learning but one thing it got right is that Weebit is showing a couple of innovative NVM demonstrations. The first is a demonstration of some of the benefits of Weebit ReRAM, a silicon-proven NVM technology that has ultra-low power consumption, high retention even at high temperatures, fast access time, high tolerance to radiation and electromagnetic interference (EMI), and numerous other advantages.

The demonstration uses Weebit’s first IP product, Weebit ReRAM IP in SkyWater Technology’s S130 process. For the demo, the ReRAM module is integrated into a subsystem with a RISC-V microcontroller (MCU), system interfaces, SRAM, and peripherals. The demo highlights the lower power consumption of Weebit ReRAM compared to typical flash memory. It also highlights the technology’s faster Write speed, which is largely due to its Direct Program/Erase capability and byte addressability. Unlike flash, which must access entire sectors of data every time it erases or writes, ReRAM only programs the bits that need to be programmed.

Weebit’s second demo is a bio-inspired neuromorphic computing demo in which Weebit ReRAM runs inference tasks using CEA-Leti’s Spiking Neural Network (SNN) algorithms. ChatGPT seemed particularly enthusiastic about this demo, saying, “Step into a realm where science fiction becomes reality as [this] mind-bending technology showcases the power of mimicking the human brain. It’s like a sci-fi movie come to life!”

This may sound over the top, but it is pretty exciting stuff. Commonly today, neural networks are simulated using traditional digital processors and accelerators, but this is inefficient and power hungry. A more efficient approach is neuromorphic computing, which makes it possible to emulate the brain’s natural operation, consuming orders of magnitude less power than today’s simulations. Because a ReRAM cell has physical and functional similarities to a biological brain synapse, it’s a natural candidate for implementing neuromorphic computing.

Visit the Weebit Nano booth #2224 at DAC to check out our demos and meet some of our execs and technologists. To arrange an in-person meeting email info@weebit-nano.com.

About Weebit Nano (ChatGPT)

Weebit Nano is an Israeli semiconductor company that specializes in the development and commercialization of next-generation memory technology. The company was founded in 2014 and is headquartered in Hod Hasharon, Israel.

Weebit Nano focuses on the development of ReRAM (Resistive Random Access Memory) technology, which is a type of non-volatile memory that has the potential to replace existing memory technologies like Flash and DRAM. ReRAM offers advantages such as faster read and write speeds, lower power consumption, and higher density compared to traditional memory technologies.

Weebit Nano’s ReRAM technology is based on silicon oxide materials and utilizes a cross-point array architecture. This allows for the stacking of multiple layers of memory cells, enabling high-density memory solutions. The company’s technology has potential applications in various fields, including consumer electronics, artificial intelligence, Internet of Things (IoT), and data storage.

Also Read:

Weebit ReRAM: NVM that’s better for the planet

How an Embedded Non-Volatile Memory Can Be a Differentiator

CEO Interview: Coby Hanoch of Weebit Nano


Podcast EP169: How Are the Standards for the Terabit Era Defined?

Podcast EP169: How Are the Standards for the Terabit Era Defined?
by Daniel Nenni on 06-30-2023 at 10:00 am

Dan is joined by Priyank Shukla of Synopsys and Kent Lusted of Intel.

Priyank Shukla is a Sr. Staff Product Manager for the Synopsys High-Speed SerDes IP portfolio. He has broad experience in analog, mixed-signal design with strong focus on high performance compute, mobile and automotive SoCs.

Kent Lusted is a Principal Engineer focused on Ethernet PHY Standards within Intel’s Network and Edge Group. Since 2012, he has been an active contributor and member of the IEEE 802.3 standards development leadership team. He continues to work closely with Intel Ethernet PHY debug teams to improve the interoperability of the many generations of SERDES products (10 Gbps, 25 Gbps, 50 Gbps and beyond). He is currently the electrical track leader of the IEEE P802.3df 400 Gb/s and 800 Gb/s Ethernet Task Force as well as the electrical track leader of the IEEE P802.3dj 200 Gb/s, 400 Gb/s, 800 Gb/s, and 1.6 Tb/s Ethernet Task Force.

Dan explores the process of developing high-performance Internet standards and supporting those standards with compliant IP. The relationships between the IEEE and other related communication standards are discussed. The complex, interdependent process of developing and validating new products against emerging standards is explored.

The views, thoughts, and opinions expressed in these podcasts belong solely to the speaker, and not to the speaker’s employer, organization, committee or any other group or individual.


TSMC Redefines Foundry to Enable Next-Generation Products

TSMC Redefines Foundry to Enable Next-Generation Products
by Mike Gianfagna on 06-30-2023 at 6:00 am

TSMC Redefines Foundry to Enable Next Generation Products

For many years, monolithic chips defined semiconductor innovation. New microprocessors defined new markets, as did new graphics processors, and cell-phone chips. Getting to the next node was the goal, and when the foundry shipped a working part victory was declared. As we know, this is changing. Semiconductor innovation is now driven by a collection of chips tightly integrated with new packaging methods, all running highly complex software. The implications of these changes are substantial. Deep technical skills, investment in infrastructure and ecosystem collaboration are all required. But how does all of this come together to facilitate the invention of the Next Big Thing? Let’s look at how TSMC redefines foundry to enable next-generation products.

What Is a Foundry?

The traditional scope of a foundry is wafer fabrication, testing, packaging, and delivery of a working monolithic chip in volume. Enabling technologies include a factory to implement a process node, a PDK, validated IP and an EDA design flow. Armed with these capabilities, new products are enabled with new monolithic chips. All this worked quite well for many decades. But now, the complexity of new product architectures, amplified by a software stack that is typically enabling AI capabilities, demands far more than a single, monolithic chip. There are many reasons for this shift from monolithic chip solutions and the result is a significant rise in multi-die solutions.

Much has been written about this shift in the innovation paradigm it enables. In the interest of time, I won’t expand on that here. There are many sources of information that explain the reasons for this shift. Here is a good summary of what’s happening.

The bottom line of all this is that the definition of product innovation has changed substantially. For many decades, the foundry delivered on the technology needed to drive innovation – a new chip in a new process. The requirements today are far more complex and include multiple chips (or chiplets) delivering various parts of the new system’s functionality. These devices are often accelerating AI algorithms. Some are sensing the environment, or performing mixed signal processing, or communicating with the cloud. And others are delivering massive, local storage arrays.

All this capability must be delivered in a dense package to accommodate the required form factor, power dissipation, performance, and latency of new, world-changing products. The question to pose here is what has become of the foundry? Delivering the enabling technology for all this innovation requires a lot more than in the past. Does the foundry now become part of a more complex value chain, or is there a more predictable way?  Some organizations are stepping up. Let’s examine how TSMC redefines foundry to enable next-generation products.

The Enabling Technologies for Next Generation Products

There are new materials and new manufacturing methods required to deliver the dense integration required to enable next-generation products. TSMC has developed a full array of these technologies, delivered in an integrated package called TSMC 3DFabric™.

Chip stacking is accomplished with a front-end process called TSMC-SoIC™ (System on Integrated Chips). Both Chip on Wafer (CoW) and Wafer on Wafer (WoW) capabilities are available. Moving to back-end advanced packaging, there are two technologies available. InFO (Integrated Fan-Out) is a chip-first approach that provides redistribution layer (RDL) connectivity, optionally with local silicon interconnect. CoWoS® (Chip on Wafer on Substrate) is a chip-last approach that provides a silicon interposer or an RDL interposer with optional local silicon interconnect.

All of this capability is delivered in one unified package. TSMC is clearly expanding the meaning of foundry. In collaboration with IP, substrate and memory suppliers, TSMC also provides an integrated turnkey service for end-to-end technical and logistical support for advanced packaging. The ecosystem tie-in is a critical ingredient for success. All suppliers must work together effectively to bring the Next Big Thing to life. TSMC has a history of building strong ecosystems to accomplish this.

Earlier, I mentioned investment in infrastructure. TSMC is out in front again with an intelligent packaging fab. This capability makes extensive use of AI, robotics and big data analytics. Packaging used to be an afterthought in the foundry process. It is now a centerpiece of innovation, further expanding the meaning of foundry.

Toward the Complete Solution

All the capabilities discussed so far bring us quite close to a fully integrated innovation model, one that truly extends what a foundry can deliver. But there is one more piece required to complete the picture. Reliable, well-integrated technology is a critical element to successful innovation, but the last mile for this process is the design flow. You need to be able to define what technologies you will use, how they will be assembled and then build and verify a model of your semiconductor system and verify it will work before building it.

Accomplishing this requires the use of tools from several suppliers, along with IP and materials models from several more. It all needs to work in a unified, predictable way. For the case of advanced multi-chip designs, there are many more items to address. The choice of active and passive dies, how they are connected, both horizontally (2.5D) and vertically (3D) and how they will all interface to each other are just a few of the new items to consider.

I was quite impressed to see TSMC’s announcement at its recent OIP Ecosystem Forum to address this last mile problem. If you have a few minutes, check out Jim Chang’s presentation. It is eye-opening.

The stated mission for this work is:

  • Find a way to modularize design and EDA tools to make the 3DIC design flow simpler and efficient
  • Ensure standardized EDA tools and design flows are compliant with TSMC’s 3DFabric technology
3Dblox Standard

With this backdrop, TSMC introduced the 3Dblox™ Standard. This standard implements a language that provides a consistent way specify all requirements for a 2.5/3D design. It is an ambitious project that unifies all aspect of 2.5/3D design specification, as shown in the figure.

Thanks to TSMC’s extensive OIP ecosystem, all the key EDA providers support the 3Dblox language, making it possible to perform product design in a unified way, independent of a specific tool flow.

This capability ties it all together for the product designer. The Next Big Thing is now within reach, since TSMC redefines foundry to enable next-generation products.

Also Read:

TSMC Doubles Down on Semiconductor Packaging!

TSMC Clarified CAPEX and Revenue for 2023!

TSMC 2023 North America Technology Symposium Overview Part 3

 


Is Your RTL and Netlist Ready for DFT?

Is Your RTL and Netlist Ready for DFT?
by Daniel Payne on 06-29-2023 at 10:00 am

Synopsys Test Family ready for DFT

I recall an early custom IC designed at Wang Labs in the 1980s without any DFT logic like scan chains, then I was confronted by Prabhu Goel about the merits of DFT, and so my journey on DFT began in earnest. I learned about ATPG at Silicon Compilers and Viewlogic, then observability at CrossCheck where I met Jennifer Scher, now she’s at Synopsys. We talked last week by video along with Synopsys Product Manager Ramsay Allen, who previously worked at UK IP Vendor Moortec, another SemiWiki client acquired by Synopsys. Test expert and R&D Director, Chandan Kumar also joined the call. Over the years Synopsys has both acquired and developed quite a broad range of EDA and IP focused on testability, so I’d say yes, they are ready for DFT.

Our discussion centered on the TestMAX Advisor tool and how it helps on testability issues that can be addressed early at the RTL stage, like:

  • DFT violation checks – ensures RTL is scan ready
  • ATPG coverage estimation – does RTL design achieve fault coverage goals
  • Test robustness – reliability in presence of glitches, Xs, edge inconsistencies
  • Test Point selection – finds hard-to-test areas
  • Connectivity validation – DFT connections at SoC assembly

The focus of this interview however was the latest test robustness and reliability capabilities that Advisor provides in the form of glitch monitoring and X capture.

Glitches

A digital circuit that produces glitches on certain nets can cause temporary errors, something to be avoided in making an IC operate robustly and reliably. Three classes of glitches can be identified automatically by TestMAX Advisor:

  • Clock Merge
  • Reset Glitch
  • DFT Logic Glitch

Here’s an example logic cone for each type of glitch:

In functional mode the designer needs to ensure that a single clock passes through the Clock Gating Cells by controlling the enabled pins, but then in test mode only one clock signal can propagate. The above example shows how two clock signals combine to create a clock merge glitch, which needs to be found and fixed before tape out.

Every violation detected by Synopsys TestMAX Advisor includes the RTL source code line number to consider changing, so designers know what is causing the issue. Tool users can even define any logic path between two points in their design to search for glitches. Glitches are painful to find, especially if they aren’t found until late in the logic design cycles or even during silicon debug. Glitches can be triggered on rising or falling edges of internal signals, so it’s paramount to discover these early in the design process when changes are much easier to make. The automated checking understands the unateness of each logic path.

Another example of glitch detection was shown when a signal called test_mode would transition.

Glitches due to Mode Transition

The actual error report for this glitch was:

Clock(s) and 1 Clock Enable(s) logic reconverges at or near test.clk_en‘.(Count of reconvergence start points = ‘1’ reconvergence end = ‘test.clk_en‘)[Affects ‘1’ Flip-flops(s)]

The final type of glitch detection was for buses driven by tri-state buffers, where clock edge inconsistencies and bus contention were caught.

Summary

RTL design and debug is a labor-intensive process, so having proper automation tools like Synopsys TestMAX Advisor are an insurance policy against re-spins caused by testability issues like glitches and Xs in an IC design. Early warning on the DFT robustness is a smart investment that pays off in the long wrong by improving the chances for a first silicon success. Design engineers run Synopsys TestMAX Advisor on every level of their hierarchical design, including the final, full-chip level.

Designers save time by using an automated checking tool, instead of relying upon manual detection methods.

For more information on Synopsys TestMAX products, please, visit the website.

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Unique IO & ESD Solutions @ DAC 2023!

Unique IO & ESD Solutions @ DAC 2023!
by Daniel Nenni on 06-29-2023 at 6:00 am

DAC photo Certus Semiconductor

The semiconductor industry continues to drive innovation and constantly seeks methods to lower costs and improve performance. The advantages of custom I/O libraries versus free libraries can be seen as cost-savings or, more importantly, new markets, new customers, and new business
opportunities.

At DAC 2023, Certus Semiconductor will share the advantages of high-performance I/O libraries and having the opportunity to collaborate on new ideas, incorporating unique features that will open new markets and new opportunities for your company.

Certus Semiconductor is a Unique IO & ESD Solution Company

Certus has assembled several of the world’s foremost experts in IO and ESD design to offer clients the ability to affordably tailor their IO libraries into the optimal fit for their products.

Certus expertise cross all industries. They have tailored IO and ESD libraries for low power and wide voltage ranges, and RF low cap ESD targeting the IoT, wireless and consumer electronics markets. There are IO Libraries customized for  flexible interface, multi-function, and high performance that target the FPGA and high performance computing markets. Certus expertise also includes radiation  hardened, high reliability and high-temperature IO libraries for the aerospace, automotive and industrial markets. Certus leverages this expertise to work directly with you – that means meeting with your architects, marketing team, circuit & layout designers and reliability engineers to ensure that the Certus IO and ESD solutions provide the most efficient and competitive solutions for your products and target markets.

Stephen Fairbanks, CEO of Certus Semiconductor has stated, “Our core advantages is our ability to truly work with our customers, understand their product goals and customer applications  and then to help them create IO and ESD solutions that give their products a true market differentiation and competitive advantage.  All our repeat business has been born out of these types of collaborations.“

Certus has silicon-proven libraries in a variety of foundry processes. These can be licensed off-the-shelf or can be customized for your application, and are available as full libraries or on a cell-by-cell basis.

In addition to these processes, Certus has consulted on designs in many others and can be contracted for development in any of them. Our foundry experience, includes all major foundries such as Samsung, Intel, TowerJazz, DongBu HiTek, UMC, pSemi, Silanna, Lfoundry, Silterra, TSI, XFab,Vanguard and many others.The Design Automation Conference (DAC) is the premier event devoted to the design and design automation of electronic chips and systems. DAC focuses on the latest methodologies and technology advancements in electronic design.  The 60th DAC will bring together researchers, designers, practitioners, tool developers, students and vendors.

Certus is one of the  more than 130 companies supporting this industry leading event and they invite you to meet with the Certus I/O and ESD experts on the exhibit floor. You can contact Certus HERE to schedule a meeting at booth #1332. I hope to see you there!

Also Read:

The Opportunity Costs of using foundry I/O vs. high-performance custom I/O Libraries

CEO Interview: Stephen Fairbanks of Certus Semiconductor

Certus Semiconductor releases ESD library in GlobalFoundries 12nm Finfet process

 


Semiconductor CapEx down in 2023

Semiconductor CapEx down in 2023
by Bill Jewell on 06-28-2023 at 2:00 pm

Semiconductor Capex 2021 2022 2023

Semiconductor capital expenditures (CapEx) increased 35% in 2021 and 15% in 2022, according to IC Insights. Our projection at Semiconductor Intelligence is a 14% decline in CapEx in 2023, based primarily on company statements. The biggest cuts will be made by the memory companies, with a 19% drop. CapEx will drop 50% at SK Hynix and 42% at Micron Technology. Samsung, which only increased CapEx by 5% in 2022, will hold at about the same level in 2023. Foundries will decrease CapEx by 11% in 2023, led by TSMC with a 12% cut. Among the major integrated device manufacturers (IDMs), Intel plans a 19% cut. Texas Instruments, STMicroelectronics and Infineon Technologies will buck the trend by increasing CapEx in 2023.

Companies which are significantly cutting CapEx are generally tied to the PC and smartphone markets, which are in a slump in 2023. IDC’s June forecast had PC shipments dropping 14% in 2023 and smartphones dropping 3.2%. The PC decline largely affects Intel and the memory companies. The weakness in smartphones primarily impacts TSMC (with Apple and Qualcomm as two of its largest customers) as well as the memory companies. The IDMs which are increasing CapEx in 2023 (TI, ST, and Infineon) are more tied to the automotive and industrial markets, which are still healthy. The three largest spenders (Samsung, TSMC and Intel) will account for about 60% of total semiconductor CapEx in 2023.

The high growth years for semiconductor CapEx tend to be the peak growth years for the semiconductor market for each cycle. The chart below shows the annual change in semiconductor CapEx (green bars on the left scale) and annual change in the semiconductor market (blue line on the right scale). Since 1984, each significant peak in semiconductor market growth (20% or greater) has matched a significant peak in CapEx growth. In almost every case, the significant slowing or decline in the semiconductor market in the year following the peak has resulted in a decline in CapEx in one or two years after the peak. The exception is the 1988 peak, where CapEx did not decline the following year but was flat two years after the peak.

This pattern has contributed to the volatility of the semiconductor market. In a boom year, companies strongly increase CapEx to increase production. When the boom collapses, companies cut CapEx. This pattern often leads to overcapacity following boom years. This overcapacity can lead to price declines and further exacerbate the downturn in the market. A more logical approach would be a steady increase in CapEx each year based on long-term capacity needs. However, this approach can be difficult to sell to stockholders. Strong CapEx growth in a boom year will generally be supported by stockholders. But continued CapEx growth in weak years will not.

Since 1980, semiconductor CapEx as a percentage of the semiconductor market has averaged 23%. However, the percentage has varied from 12% to 34% on an annual basis and from 18% to 29% on a five-year-average basis. The 5-year average shows a cyclical trend. The first 5-year average peak was in 1985 at 28%. The semiconductor market dropped 17% in 1985, at that time the largest decline ever. The 5-year average ratio then declined for nine years. The average eventually returned to a peak of 29% in 2000. In 2001 the market experienced its largest decline ever at 32%. The 5-year average then declined for twelve years to a low of 18% in 2012. The average has been increasing since, reaching 27% in 2022. Based on our 2023 forecasts at Semiconductor Intelligence, the average will increase to 29% in 2023. 2023 will be another major downturn year for the semiconductor market. Our Semiconductor Intelligence forecast is a 15% decline. Other forecasts are as low as a 20% decline. Will this be the beginning of another drop in CapEx relative to the market? History shows this will be the likely outcome. Major semiconductor downturns tend to scare companies into slower CapEx.

The factors behind CapEx decisions are complex. Since a wafer fab currently takes two to three years to build, companies must project their capacity needs several years into the future. Foundries account for about 30% of total CapEx. The foundries must plan their fabs based on estimates of the capacity needs of their customers in several years. The cost of a major new fab is $10 billion and up, making it a risky proposition. However, based on past trends, the industry will likely see lower CapEx relative to the semiconductor market for the next several years.

Also Read:

Steep Decline in 1Q 2023

Electronics Production in Decline

Automotive Lone Bright Spot


Better Randomizing Constrained Random. Innovation in Verification

Better Randomizing Constrained Random. Innovation in Verification
by Bernard Murphy on 06-28-2023 at 10:00 am

Innovation New

Constrained random methods in simulation are universally popular, still can the method be improved? Paul Cunningham (Senior VP/GM, Verification at Cadence), Raúl Camposano (Silicon Catalyst, entrepreneur, former Synopsys CTO and now Silvaco CTO) and I continue our series on research ideas. As always, feedback welcome.

The Innovation

This month’s pick is Balancing Scalability and Uniformity in SAT Witness Generator. A refined version of the paper was published by Springer in 2015 in “Proceedings of the 21st International Conference on Tools and Algorithms for the Constructions and Analysis of Systems”. The authors are/were from IIT Bombay and Rice University, Houston TX.

Constrained random effectiveness depends on the quality of constraints, also the uniformity of the distribution generated against those constraints since uneven distributions will bias away from triggering bugs in lightly covered states. Unfortunately generation methods tend either to high uniformity but impractical run-times for large designs or to good scalability but weak uniformity. The authors’ paper describes a method to provide approximate guarantees of uniformity with better performance than prior papers on the topic.

The authors also reported a refinement to this method in a later publication apparently only available from Springer-Verlag.

Paul’s view

A key engine at the heart of all commercial logic simulators is the constraint solver, responsible for picking pseudo-random input values each clock cycle in a constrained-random test. These solvers must be ultra-fast but also pick a good spread of random values across the solution space of the constraints. For the scale of constraint complexity in modern commercial SOC tests this is a really hard problem and EDA vendors have a lot of secret sauce in their solvers to tackle it.

Under the hood of these solvers, constraints are munged into Boolean expressions, and constraint solving turns into a Boolean SAT problem. In formal verification we are trying to find just one solution to a massive Boolean expression. In constrained-random simulation we are trying to find a massive number of “uniformly distributed” solutions to smaller Boolean expressions.

The way solvers achieve uniformity is conceptually simple: partition the set of all solutions into n groups of roughly equal size, first pick a random group and then find a solution that belongs to that group. This forces the solver to spread its solutions over all the groups, and hence over the solution space. Implementing this concept is super hard and involves ANDing the Boolean expression to be solved with a nasty XOR-based Boolean expression encoding a special kind of hash function. This hash function algebraically partitions the solution space into the desired number of groups. The smaller the groups (i.e. the larger n is) the more uniform the solver, but the slower the solver is, so picking the right number of groups is not easy and must be done iteratively.

There are two key innovations in this paper: one relates to dramatically reducing the size of the XOR hash function expression, the other to dramatically reducing the number of iterations required to get the right group size. Both innovations come with rigorous proofs that the solver still meets a formal definition of being “uniform”. It’s impressive work and way too complex to explain fully here, but the results are literally 1000x faster than prior work. If you have the energy to muscle through this paper it is well worth it!

Raúl’s view

A “SAT witness” is a satisfying assignment of truth values to variables such that a Boolean formula F evaluates to true. Constraints in Constrained Random Verification (CRV) of digital circuits are encodable as Boolean formulae, so generation of SAT witnesses is essential for CRV. Since the distribution of errors in a design is not known a priori, all solutions to the constraints are equally likely to discover a bug. Hence it is important to sample the solution space uniformly at random, meaning that if there are RF SAT witnesses, the probability PR of generating a value is 1/RF; the paper uses “almost” uniformly defined as 1/(1+ℇ)RF ≤ PR ≤ (1+ ℇ)/RF. Uniformity poses significant technical challenges when scaling to large problem sizes. At the time of publication of the paper (2014) the proposed algorithm, UniGen, was the first to provide guarantees of almost-uniformity and scaling to hundreds of thousands of variables.  The algorithm is based on 3-independent hash-functions and it is beyond the scope of this blog to delve into it; the reader is referred to section 4 in the paper.

The experimental results show that for problems with up to 486,193 variables witnesses can be generated in less than 782 secs with a probability of 0.98 of success. Comparisons with UniWit, the state-of-the-art at the time, show runtimes that are 2-3 orders of magnitude lower. UniGen manages all 12 examples, while UniWit only can complete 7 out of 12. Uniformity is shown by comparing UniGen to a uniform sampler that simply picks solutions out of RF randomly (Figure 1).

The industry has been moving towards increased automation and advanced verification methodologies, making commercial CRV tools more prevalent, particularly for complex digital designs. Constraint solving techniques have been a fundamental part of CRV. Recent advances have focused on improving constraint solving algorithms, optimizing random test generation, and addressing scalability challenges. And although much progress has been achieved since 2014, the reviewed paper (cited 81 times) is an excellent example that illustrates these advances.

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Clock Verification for Mobile SoCs

Clock Verification for Mobile SoCs
by Daniel Payne on 06-28-2023 at 6:00 am

Clock duty cycle distortion

The relentless advancement of mobile phone technology continues to push boundaries, demanding SoCs that deliver ever-increasing performance while preserving extensive battery life. To meet these demands, the industry is progressively embracing lower technology nodes with current designs being taped-out at 5nm or below. Designing and verifying clocks at these lower geometries brings mounting complexities and increasing verification challenges. In this rapidly evolving landscape, current clock verification methodologies must be reassessed to ensure optimal clock performance and reliability.

The existing clock methodologies primarily rely on Static Timing Analysis (STA) as a standalone solution or a more advanced approach, that combines STA with a SPICE simulator to analyze critical paths.  This flow necessitates the involvement of a CAD department to establish the flow and a strict methodology to produce accurate and timely results, but even then, for an SoC level clock signal at a lower process node, the simulator may lack capacity and/or accuracy requirements. Moreover, the identification of critical paths relies heavily on the judgment and experience of engineers. This approach leads to unnecessary guard-banding, leaving valuable timing margin untapped and limiting overall performance.

At the 7nm, 5nm and 3nm process nodes both the transistor and interconnect dimensions are reduced, resulting in sensitivities to a variety of design and process-related issues, like rail-to-rail failures and duty cycle distortion in the clock signal.

Rail-to-rail Failures

If a clock net has a weak driver, long interconnect and large capacitive loading, then it can lead to increased insertion delays, and worst-case a rail-to-rail failure. The voltage levels on the clock simply don’t reach the VSS and VDD levels in a rail-to-rail failure. Running STA alone will not detect this failure mechanism because STA measures timing at specific voltage thresholds.

An increase in clock frequency reduces clock period, resulting in a shorter time window for the clock to reach the supply rail voltage levels. Voltage scaling also makes the clock signal more vulnerable to rail-to-rail failure, as the smaller gap between the supply and Vth leads to increase in non-linear operation, reducing the drive strength. Even process variations in Vth, transistor W and L variations, or parasitic capacitances will contribute to rail-to-rail failure. Local power supply levels will bounce around from IR drop effects, which then degrade signal levels and timing in the clock signal.

Clock rail-to-rail failure detection

Clock duty cycle distortion

When a clock signal propagates through a series of gates with asymmetric pull-up and pull-down drive strengths, then it causes duty-cycle-distortion (DCD). An ideal duty cycle for a clock is 50% low and 50% high pulse width. Increased clock frequencies can amplify timing imbalances and cause signal integrity issues like DCD. Clock interconnect is impacted by capacitive and resistive effects, which change the slew rate for rise and fall times, delaying the clock and causing asymmetry, making DCD effects more pronounced. Process variations directly alter interconnects, adding imbalances in circuit timing, adding to DCD.

Clock duty cycle distortion

For process nodes with asymmetric PVT corners the DCD becomes more pronounced. Results from a STA tool are focused on insertion delay, so it is less accurate to report DCD and Minimum-Pulse-Width (MPW).

Slew Rate and Transition Distortion

At lower process nodes, the parasitic interconnect has more pronounced resistive-shielding and capacitive coupling, degrading slew rate and clock edge transitions. STA tools use a simplified model for interconnect parasitics which can then underestimate the clock signal degradations.

Power-supply induced jitter

Noise in the Power Delivery Network (PDN) impacts clock timing, producing jitter which impacts clock performance.  When the power supply experiences fluctuations or noise, it can introduce voltage variations that directly affect the clock signal’s stability and integrity. Power supply induced jitter can lead to timing errors in clock signals, causing them to arrive earlier or later than expected. This can result in setup and hold violations, leading to potential functional failures in the clock.  The increased jitter can also reduce the timing margin, making the design more susceptible to timing violations and potential performance degradation. STA tools primarily focus on analyzing the timing behavior of a design based on a static representation of the circuit and cannot do Jitter. Designers typically use an approximation for jitter effects, so it is really just another guard-band approach.

Power Supply Noise

Topologies using clock grids and spines

Grid and Spine architecture, especially at 7nm and below technology nodes can offer significant advantages including enhanced signal integrity and power and area efficiency.  Grid and spine structures provide a regular and structured framework for routing clock signals, reducing the impact of the increased process variations of lower technology nodes, improving signal integrity and mitigating issues like clock skew, jitter and noise. In addition, grid and spine architecture allows for optimized routing of clock signals.

Circuit simulation is the only accurate method to verify grids and spines, but most commercial SPICE simulators do not handle the capacity for such large meshes.  Designing a lower technology node clock with grids and spines without an adequate, fast and accurate verification methodology can be a risky proposition.

Summary

Mobile devices require mobile processors, and they often drive the bleeding-edge of IC process technology. Meeting PPA goals in a timely manner is paramount to the success of mobile SoCs. At 7nm and below technology nodes, a fresh approach to clock verification becomes imperative. Failing to adopt such an approach entails increased guard-banding, leading to increased area and power requirements. Most importantly, the conservative nature of guard-banding, leaves valuable performance on the table.

Enter Infinisim’s ClockEdge, an off-the-shelf solution specifically engineered for thorough clock verification and analysis. ClockEdge boasts an exceptional ability to analyze every path within the entire clock domain with SPICE-level accuracy. This has the potential to unlock unparalleled analysis opportunities that are otherwise unattainable using conventional methodologies. Moving to 7nm and below technology node is a costly endeavor, yet it offers significant benefits in Power, Performance and Area (PPA) efficiency. However, guard-banding practices can diminish these advantages. Infinisim’s solution identifies all potential failures and optimizes PPA by minimizing the need for excessive guard-banding, thus capitalizing on the advantages afforded by a move to a lower technology node.

With a well-established reputation, Infinisim has a proven track record in the industry. Their solutions have been adopted as a sign-off tool by their mobile SoC customers, solidifying their position as a trusted partner. Infinisim’s expertise in clock analysis spans a wide range of designs, from 28nm to the most advanced 3nm process node. They provide extensive support for all major foundries, including TSMC, Samsung and GlobalFoundries.

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Samsung Foundry on Track for 2nm Production in 2025

Samsung Foundry on Track for 2nm Production in 2025
by Daniel Nenni on 06-27-2023 at 3:00 pm

Samsung Foundry Forum 2023

On the heels of the TSMC Symposium and the Intel Foundry update, Samsung held their Foundry Forum today live in Silicon Valley. As usual it was a well attended event with hundreds of people and dozens of ecosystem partners. The theme was the AI Era which is appropriate. As I have mentioned before, AI will touch most every chip and there will never be enough performance or integrated memory so leading edge process and packaging technology is foundry critical, absolutely.

Samsung Foundry has always met customer needs by being ahead of the technology innovation curve and today we are confident that our gate-all-around (GAA)-based advanced node technology will be instrumental in supporting the needs of our customers using AI applications,” said Dr. Siyoung Choi, president and head of Foundry Business at Samsung Electronics. “Ensuring the success of our customers is the most central value to our foundry services.”

The Samsung Foundry market breakdown for 2022 was not surprising:

  • Mobile 39%
  • HPC 32%
  • IoT 10%
  • Consumer 9%

Moving forward however HPC is expected to dominate the foundry business ( > 40% ) as AI takes more than their fair share of leading edge wafers.

The most significant announcement was that Samsung 2nm is on track to start production in 2025, which was the date given at the previous Samsung Foundry Forum. Staying on track with the published roadmap is a big part of foundry trust. Remember, if a fabless company is going to bet their company jewels on a foundry partnership they have to trust that the wafers will be delivered on time matching the PDK specifications.

Highlights include:
  • Expanded applications of its 2-nanometer (nm) process and specialty process
  • Expanded production capacity at its Pyeongtaek fab Line 3
  • Launched a new ‘Multi-Die Integration (MDI) Alliance’ for next-generation packaging technology

At the event, Samsung announced detailed plans for the mass production of its 2nm (horizontal nanosheet) process, as well as performance levels. Samsung, like Intel, are their own foundry customer so first production is with internal products versus external foundry customers. This of course is the advantage of an IDM foundry, developing your own silicon in concert with process technologies. Samsung has the added advantage of developing leading edge memories.

Samsung will begin mass production of the 2nm process for mobile applications in 2025, and then expand to HPC in 2026 with backside power delivery, and automotive in 2027. Samsung’s 2nm (SF2) process has shown a 12% increase in performance, a 25% increase in power efficiency, and a 5% decrease in area, when compared to its 3nm process (SF3). Mass production of the follow-on 1.4nm is slated for 2027.

TSMC overwhelming won the 3nm node with the N3X process family, however, the 2nm node is undecided. TSMC N2, Intel 18A, and Samsung 2nm are very competitive on paper and should be ready for external customers in the same time frame. It will all depend on how the PDKs proceed. According to the ecosystem, customers are looking at all three processes so it is a three horse race which is great for the foundry business. No one enjoys a one horse race except for that one horse.

The other big announcement was packaging, another advantage of an IDM foundry. Intel and Samsung have been packaging chips before foundries existed. Now they are opening up their packaging expertise to external foundry customers. We will be writing more about packaging later but it is a very big opportunity for foundries to empower customers.

For packaging Samsung announced the MDI Alliance in collaboration with partner companies as well as major players in 2.5D and 3D,  memory, substrate packaging, and testing. Packaging is now a very important part of the foundry business. With the advent of chiplets and the ability to mix and match die from different processes and foundries, packaging is a new foundry arms race and it is good to see three strong horses competing for our business.

This was an excellent networking event, the food is always great, and the Samsung people are very polite and professional. Samsung Foundry will be at DAC 2023 in San Francisco the week of July 9th. I hope to see you there.

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