Artificial Intelligence

Machine learning (ML) and artificial intelligence (AI) have ushered in tremendous opportunities for faster growth, problem-solving and technological development in the electronic system design ecosystem. Cadence Design Systems, Inc., a member of the ESD Alliance, a SEMI Technology Community, is at the technological forefront in incorporating ML techniques in its chip design products. I spoke with Chin-Chi Teng, Senior Vice President and General Manager of Cadence’s Digital Signoff Group, about how ML is reshaping EDA and the semiconductor industry, the cloud’s role in the evolution of ML in design and its impact on Moore’s Law. Teng also offers advice on how engineering students can calibrate their education to prepare to work with this transformative technology and urges them to have fun in the process. Smith: How is ML changing the EDA industry? Teng: ML is changing EDA for the better in many ways. It’s more difficult than ever to design chips, and ML is helping by overcoming the complexity, size and technology interdependencies. At the same time, ML is helping our own engineers solve certain classes of EDA algorithm, tool, and flow/solution challenges so that we can deliver even better EDA tools to our user base. The benefits can include reducing runtime, increasing quality of results, and being better equipped to manage vast complexity and data. Also, and maybe even more significant, is the potential boost to user and team productivity, where engineers have more time to focus on high-value problems because they no longer need to spend time on managing overwhelming volumes of data and details that can be easily automated. Smith: What is the potential impact ML can have on semiconductor design? Teng: ML technology can be leveraged in several ways to improve EDA tool performance and engineering team productivity. For example, we initially applied ML to applications such as formal verification, simulation regressions, analog circuit design, and PCB design. We targeted ML toward specific algorithms that processed lots of data to sharpen and speed decision-making. Then we started to look at digital implementation flows that combine multiple steps with multiple decisions in a recipe, especially for chip implementation where the more efficient use of engineering knowledge can make a substantial difference in the chip’s resulting power, performance and area (PPA). These flows present more challenges and require different ML and optimization techniques since the data points are expensive to create and the volume of data is huge. But flow optimization offers the largest rewards for companies investing in data collection and analysis to improve their operations and product quality. By using ML to improve the implementation flow, our users are seeing up to 20% better PPA and 10x improved productivity in developing data center CPUs and AI engines, automotive sensor processing SoCs, and mobile devices. Smith: What is the cloud’s role in the evolution of ML in EDA? Teng: More ML usage means there will be an inevitable surge in compute demand resources, and engineers need the ability to scale in parallel. The cloud provides engineers with the best opportunity to scale computing resources without facing procurement limitations. The cloud also allows engineers to use task-specific compute and ML accelerators and capitalize on distributed computing innovations that leverage the cloud for greater design flexibility and availability. Smith: You have written that you see Moore’s Law accelerating. How does ML fit into this? Teng: We see the rapid adoption of new process technologies as the biggest trend surrounding Moore’s Law right now. ML technology in EDA will help speed tool certification processes, process design kit (PDK) development and other deliverables aimed at creating and improving customer support through all stages of the process lifecycle. This is a virtuous circle, and it’s expanding beyond hardware design and optimization to also include software. Today’s ML functionality works on the abstraction of register transfer level (RTL), optimizing the implementation and verification flows. ML will soon enable use of a higher abstraction of describing the target systems, exploring architectural options and optimizing across hardware and software partitioning. Smith: What advice would you give engineering students who are studying ML with the goal of becoming an electrical engineer? Teng: With the rapid pace of technology development, things are changing constantly. I’d absolutely encourage students to look at ML because ML isn’t going away — its growth is only going to accelerate from here. I’d also suggest that students look more broadly at computational mathematics because that’s foundational for ML. There are many, many opportunities to apply ML to real-world applications that will make a significant impact when it comes to optimizing computational software. Most important, students should explore and have fun while doing it. About Chin-Chi Teng Chin-Chi Teng has served as Senior Vice President and General Manager of the Digital and Signoff Group (DSG) since 2018. Prior to this role, Teng held senior leadership positions in research and development in digital implementation. Teng joined Cadence in 2002 via the acquisition of Silicon Perspective Corporation and subsequently led various research and development groups. He brought deep technical knowledge and more than 20 years of industry and academic experience to his role as leader of the IC Digital group. Teng holds a BS in electrical engineering from the National Taiwan University and an MS and Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign. He holds seven patents and has written many EDA papers, several deep learning papers, and the book Electrothermal Analysis of VLSI Systems. Robert (Bob) Smith is executive director of the ESD Alliance, a SEMI Technology Community.

Alameda, Calif.-based Verific Design Automation, a member of the ESD Alliance, made its name in the electronic system design and semiconductor industry supporting companies ranging from startups to billion-dollar industry leaders such as Synopsys, Cadence, Siemens EDA, Xilinx, Microchip, NVidia, Infineon, Qualcomm, Renesas and Samsung. Its software is used as the front end to design automation tools such as synthesis, simulation, debug, and formal verification. I spoke with Verific president and COO Michiel Ligthart about homegrown and open-source EDA tools and other recent trends in chip design. Smith: What trends are you seeing in chip design? Ligthart: Semiconductor companies are starting to build a portfolio of intellectual property, including homegrown electronic design automation (EDA) tools, that they want to keep secure and differentiated from their competitors. The increased interest in internally developed and supported EDA tools is a trend we started to see about two years ago. It’s not simulation, synthesis or place and route (P R). Instead, it’s pieces of a chip design flow optimized for a company’s specific needs. In the past, a semiconductor company would either standardize on one EDA company’s chip design flow or mix and match best-in-class tools from different vendors. The common denominator was that they used off-the-shelf products. If they had a specific requirement, they went to the EDA provider for assistance. In today’s competitive landscape, semiconductor companies are figuring out ways to diversify themselves and their design flow became a way to do so. They may not build their own P R tool, but they will look at building their own power domain approach, for example. Is this a widespread trend? It could be. We hear about it within end-user applications ranging from 5G and AI to data center processors and there are probably others we don’t hear about. Power optimization is an example of the kind of specific internal need being addressed. Smith: What are your thoughts about open-source EDA tools? Ligthart: Our industry supports open source already with language reference manuals (LRMs) for VHDL, SystemVerilog, Unified Power Format (UPF) and the RISC-V Instruction Set. The LRMs and the instruction set are free. Moving to the development of actual tools becomes a question of who will implement, support and maintain the tools. Implementation is expensive. The Big Three (Cadence, Siemens EDA and Synopsys) invest about 35 to 40% of top-line revenue into R D. For smaller EDA companies, this number is even higher. The industry may come up with a business model that will have open-source components as well as a way to fairly reimburse companies that make these tools freely available. I have not seen it yet. Smith: Business Insider reports that Verilog HDL is among the top 10 tech skills that companies are desperate for their employees to learn right. Does Verific get asked about Verilog training? Ligthart: No. Our customers are experienced users. Nonetheless, it was great to read that article and it suggests the semiconductor industry is healthy, growing and hiring talented engineers. Smith: If an entrepreneur asked you for advice about starting an EDA or IP company, what advice would you provide? Ligthart: I would tell the entrepreneur to focus on the problem the startup is solving. Stick to the company’s core competency and try not to build in-house what can be purchased from a reputable supplier. In the end, it will save time and jump-start the development effort, and the engineering budget can be allocated to the startup’s core competency. The external supplier presumably has years of product validation, which brings a major QA gain. About Michiel Ligthart Michiel Ligthart, president and COO of Verific Design Automation, has an extensive background in engineering, product marketing and general management. Prior to joining Verific, Ligthart was vice president and general manager of West Coast operations for Theseus Logic, a startup in asynchronous logic. Before that, he spent eight years with Exemplar Logic in engineering and marketing roles. Ligthart started his career with Philips Research Labs in California and was a visiting scholar at the Center for Integrated Systems at Stanford University. He has a Master of Science degree in Electrical Engineering from Delft University of Technology, the Netherlands. Robert (Bob) Smith is executive director of the ESD Alliance, a SEMI Technology Community.

China’s semiconductor sector has exploded to become one of the largest in the world and match the region’s prominence as the biggest consumer of chips. From 2015 to 2020 alone, revenue of China’s integrated circuit (IC) industry expanded at a rapid clip of 20% CAGR (RMB) to $128 billion.

Demand for hi-tech manufactured goods is at an all-time high and is expected to grow significantly in our new digital age, COVID-19 economy. This is especially true for semiconductor chips. Chip manufacturers have been working to meet this demand by building new factories and by optimizing processes and equipment in existing fabs. While there is much media coverage about new factories planned by leading-edge chipmakers and government investments in the semiconductor sector, greenfield fabs entail significant capital expenditures and are sometimes fraught with complex political concerns. As a result, they can take several years to complete and reach their planned production capacity. Instead, the semiconductor industry needs to optimize existing factories in order to increase productivity and yield and meet growing demand by implementing smart manufacturing solutions. Smart manufacturing solutions will inherently reduce costs with more efficient and automated processes, and those savings can be reinvested for the next wave of solutions. Chip Industry on the Bleeding Edge Semiconductor manufacturers have always been focused on bleeding-edge technology to outflank strong competition and build the best products – faster and cheaper. Today, pioneering organizations are using data to optimize manufacturing processes and equipment, a practice known as Smart Manufacturing. While there are many definitions of Smart Manufacturing, the essence is maximizing the utility of big data generated in these factories by leveraging three pillars: Sensing, Connecting, and Predicting. It is not just the digitization in manufacturing, but it is also about turning the data into actions that generate value – an effort the SEMI Smart Manufacturing Committee is driving based on the three pillars. Optimizing return on investment is the ultimate goal. SEMI Smart Manufacturing Initiative activity is based on three pillars that support the goal of increasing ROI. Making the Right Decision, Faster Smart manufacturing practices enable organizations to make the right decisions and take action faster based on insights generated from real-time and historical data. This requires data management technologies and applications that can process, analyze, and act on information instantly. It has become ever more difficult to process and discern the relevant data or signal from the vast volume of data, perform analytics or develop new ML or AI analytic tools, and then make the critical decisions to solve problems as close to real-time as possible. Who’s Responsible – IT or OT? In the past IT (Information Technology) and OT (Operations Technology) were separate entities within organizations, with IT focused on storing large amounts of data for enterprise systems and OT concentrated on using data to perform specific functions. Smart Manufacturing often demands combining IT and OT, difficult in rigid organizations that operate the two organizations independently and lack the infrastructure to implement comprehensive solutions. Success requires executive leadership sponsorship, motivated technical personnel and, most importantly, a clear deliverable on the value in implementing Smart Manufacturing. Many organizations have introduced top-level leadership positions such as a Chief Information Officer or Chief Data and Analytics Officer to address this convergence and many of these leaders are embracing Smart Manufacturing practices. The SEMI Smart Manufacturing community includes many of these leaders and therefore has highlighted the importance in the return on investment for Smart Manufacturing solutions. Read more about IT and OT convergence and note that Smart Manufacturing is synonymous with Industry 4.0. The SEMI Smart Manufacturing Initiative covers the entire supply chain. Get Smart in Smart Manufacturing While new technologies and applications are being created to deal with mountains of data, it is the underlying methodologies and practices that are key to a successful Smart Manufacturing deployment. SEMI, the trade association representing the electronics manufacturing and design supply chain, is in a perfect position to evangelize Smart Manufacturing experiences and best practices for the entire manufacturing community. The more than 30 member companies participating in the SEMI Smart Manufacturing Initiative bring more than 500 years of collective experience and knowledge to the topic. Many segments of the supply chain participate in the SEMI Smart Manufacturing Initiative including packaging, assembly, SMT and PCB assembly, test, software, data management, sensor and material suppliers. Learn How to Manufacture Smarter SEMI SMART Manufacturing is hosting two great conferences in the coming months – the Global Smart Manufacturing Conference (GSMC) and the SEMICON West Smart Manufacturing Pavilion. As a leader of the organizing committee and chair for the SEMICON West Smart Manufacturing Pavilion, I encourage people who want to learn how to implement Smart Manufacturing or expand their knowledge of Smart Manufacturing to attend these events. The GSMC will feature keynotes highlighting the value of Smart Manufacturing, offer tutorials on the three pillars, and introduce several case studies for each of the pillars. Thirty-two organizations – ranging from global cloud providers, semiconductor factory operators, leading equipment vendors and software application solution companies – will present. See the full agenda here. The SEMICON West Smart Manufacturing Pavilion will compliment GSMC by showcasing a number of use cases that highlight the value of Smart Manufacturing. Panel discussions will deep dive into the challenges of implementing these best practices and the direction smart manufacturing is taking in the coming years. Our goal for these events is for you to take this knowledge back to your companies, implement and improve on the detailed solutions highlighted at the conferences, and return next year to share your success stories with the community. See you soon, in person or virtually! About the Author Bill Pierson is VP of Semiconductors and Manufacturing at KX, leading the growth of streaming data analytics in this vertical. Bill is also a chair for the SEMICON West Smart Manufacturing Conference and an active team member of the SEMI Americas Chapter. He has extensive experience in the semiconductor industry including previous experiences at Samsung, ASML and KLA. Bill specializes in applications, analytics, and control. He lives in Austin, Texas, and when not at work can be found on the rock-climbing cliffs or at his son’s soccer matches.

Today, AI algorithms are primarily run at large data centers-- in the cloud. For this intelligence to be used at the edge, data must be transmitted to the cloud, analyzed there, and the results transmitted back to the field of operation, such as a mobile phone, medical implant or autonomous vehicle.

Throughout the current millennium, System-on-Chip (SoC) has been the gold standard for optimizing performance and cost of complete electronic systems. By incorporating practically all the phone’s digital plus analog capabilities onto a single, giant chip, the mobile phone processor serves as a near-perfect exemplar of SoC. But today’s leading integrated circuits (IC) are pushing up against the upper limit of a chip’s size which is limited by the manufacturing equipment’s optical reticle size. This has proven difficult to increase and has grown only slowly over the years. Yet market pressure continues unabated for bigger, more capable electronic systems with more integrated memory, more digital logic, and more analog/mixed signal circuitry. An emerging solution to this tension is 3D and 2.5D multi-die chip assemblies – often referred to as 3D-IC. The key technology breakthrough of 3D-IC is that it makes it possible to spread a system out over multiple, smaller chips that are then assembled close together and interconnected with high-speed, low-power interconnect technologies. By abandoning the need to integrate an entire system on a single SoC and instead allowing it to be disaggregated over multiple chips, 3D-IC enables Moore’s Law to break through the reticle size barrier, improves yield by shrinking the size of individual chips, and makes it possible to mix different process technologies optimized for each function. The Four Engines Driving Semiconductor Design The road forward is not without its challenges, however, and we are seeing design companies making significant efforts to adapt and come to grips with the following four technology and market drivers: The requirement for concurrent multiphysics analysis to ensure reliable and efficient electronic systems The blurring of the lines between silicon and system The need for open and inclusive multiphysics platforms that interoperate with the multitude of design platforms The need for, and value of, bespoke silicon for hyperscalers and system companies Blurring of Silicon and System Design The advent of 3D-IC opens up new horizons for solutions that can be implemented in silicon. But it also forces a closer integration between two distinct technology markets that have co-existed symbiotically for many decades: IC design and printed circuit board (PCB) design. These markets use different tools, different data formats, different manufacturing back-ends, operate at different computational and geometric scales, and focus on different physical concerns. Yet, 3D-ICs share many aspects of both markets: They include monolithic chips but also board-like substrates to stitch the chips together. And in between the two disciplines is packaging, a completely different domain that is requiring companies to re-imagine their design capabilities and flows, as well as their organizational structure. Open, Extensible Multiphysics Platforms The siloed isolation of chip design from PCB design and package design means that each of these markets has developed insular data structures that are ill-suited to deal with the breadth of multiphysics analysis for 3D-IC design. Many different physical disciplines, including computational fluid dynamics, mechanical stress, and electromagnetic radiation, all need to work together based on open and extensible multiphysics platforms. These platforms must embrace the modern cloud compute paradigm and enable an ecosystem by allowing individual design platforms to connect for comprehensive multiphysics analysis. Bespoke Chips Today’s market-leading companies are heavily dependent on technology for their continued success and market differentiation. Everybody from online retailers to telecommunications to social networking companies and hyperscalers are moving away from off-the-shelf solutions and turning to custom-built silicon to give them an edge. Many of these companies are seeking to gain market share by leveraging proprietary AI/ML algorithms trained on their extensive troves of market data – but this requires huge amounts of compute power and specialized chips. Access to high-quality silicon solutions is vital in today’s world and the demand is for continually more complex and powerful electronics. 3D-IC an Inflection Point in Electronic Design To be sure, 3D-IC design is at an inflection point in electronic design and presents major challenges that are realigning the electronic design industry around this new reality. For more insights on this topic from a semiconductor industry leader, please view the Keynote Address 2.5D and 3D – The Road Ahead by Vicki Mitchell, VP Engineering, Arm Central Engineering Systems Group presented at the latest Ansys IDEAS Forum. And for an EDA perspective, please view Successful 2.5D and 3D Multi-die Silicon System Design Using Synopsys’ 3DIC Compiler and Ansys’ Multiphysics Analysis from Synopsys SNUG World 2021. About John Lee John Lee is general manager and vice president of the Ansys Electronics and Semiconductor Business Unit. Lee co-founded and served as CEO of Gear Design Solutions (now Ansys), developer of the first purpose-built big data platform for integrated circuit design. He cofounded two other startups (Mojave Design and Performance Signal Integrity), which successfully exited into companies now part of Synopsys. He holds undergraduate and graduate degrees from Carnegie Mellon University.

As monolithic scaling slows down, the semiconductor industry is increasingly relying on advanced packaging technologies to extend Moore’s law through heterogeneous integration. Higher on-package bandwidth, improved yield resiliency and the need to integrate diverse IP from multiple foundries are driving demand for advanced packaging technologies that address these issues but introduce challenges of their own such as efficient power delivery to all the different domains in a heterogeneous system. SEMI spoke with Kaladhar Radhakrishnan, Intel Fellow at Intel, about heterogeneous system integration trends and new developments in the semiconductor industry. Radhakrishnan shared his views ahead of his keynote at the SEMI Connecting Heterogeneous Systems Summit, 1-3 September 2021, an online event. Join the summit to meet experts from Intel and other key industry influencers. Registration is open. SEMI: What is driving the adoption of electronics and semiconductor devices nowadays and why is the development of new and innovative technologies important? Radhakrishnan: We are living in an increasingly data-driven world where devices have become an integral part of our lives. A recent study estimated that in the United States alone, 13.6 connected devices per capita consume an average of 300 gigabytes worth of data every month. In the workplace, COVID-19 has driven fundamental business changes that has sped up the adoption of digital technologies such as virtual conferencing, remote work, and e-commerce. Organizations are realizing that a high-quality video conference can be an adequate substitute for many in-person meetings. As a result, businesses are accelerating the digital transformation in order to adapt and thrive in this new environment. Five decades of sustained exponential growth in semiconductor performance has conditioned the average digital consumer to expect more from their devices. However, there are some headwinds ahead as traditional scaling slows down and power density rises. Because consumers and businesses are now generating data at a faster rate than they can consume it, technologists need to scale compute, storage, and bandwidth even faster to keep pace. Without investments in research and development of new and innovative technologies to address these challenges, the full potential of this data will go unrealized. SEMI: What forces are heightening the importance of heterogeneous system integration? What are the implications for increased on-package bandwidth, improved yield resiliency and the need to integrate diverse IP from multiple foundries? Radhakrishnan: The semiconductor industry increased transistor density and scaled performance through classical Dennard scaling until the turn of the century. By then, the gate oxide thickness had scaled down to atomic dimensions and the exponential increase in sub-threshold leakage signaled the end of scaling through traditional methods. Since that time, the chip industry has been relying on innovations in transistor materials and structures such as high-k metal gate, strained silicon, and FinFETs to keep pace with Moore’s law. However, this alone will not be sufficient to continue scaling and the industry needs to explore other vectors to augment improvements in transistor technology. Heterogeneous integration through advanced packaging is one key technology that can help drive these gains. Technologies like Foveros can enable device density scaling by creating a 3D stack of multiple die using high-density interconnects. Heterogeneous integration enables chipmakers to move from a monolithic system designed on a single large chip to a heterogeneous system comprised of a number of smaller chiplets. The main benefit of using smaller chiplets is that they improve yield and enable application based customization of the foundry processes. However, if the disaggregation to smaller chiplets is not accompanied by an increase in on-package bandwidth, the power and performance penalties associated with chiplet-to-chiplet communication will hobble system performance. This is why advanced packaging technologies that improve die-to-die communication are key enablers for heterogeneous integration. SEMI: What are some of the key technology challenges in developing heterogeneous systems? Radhakrishnan: The obvious challenge that most people focus on is the need for improved on-package bandwidth. However, as we rely on 3D stacking to continue device scaling at the package level, it is important to comprehend power delivery and thermal challenges as well. Power to the top die has to be delivered through TSVs on the bottom die, which not only adds resistance but also reduces the useful area available on the bottom die. This problem is further exacerbated when we stack more than two die. Excessive noise on the power delivery network can cause timing issues that limit the maximum operating frequency of the transistor. Similarly, when we stack multiple die, we must take into account associated thermal challenges. For example, each interface of the multi-die stack adds thermal resistance, which makes it harder to cool the chips at the bottom. SEMI: What are some of the key global market trends that driving demand for heterogeneous and system-level integration? Radhakrishnan: The number of artificial intelligence (AI) and machine learning applications have grown dramatically due to their ability to solve highly complex problems across a wide range of segments. AI and machine learning models require more memory bandwidth and compute capabilities that are difficult to achieve without some form of heterogeneous integration. Another market trend driving demand for heterogeneous integration is the increasing reliance on custom hardware accelerators. To combat the slowdown in frequency scaling and single-core performance, we have moved to multi-core architectures by tackling the inherent parallelism in our workloads. However, Amdahl’s law tells us that such an approach will hit a bottleneck when we reach the limits of the serial portion of the workload. As these constraints slow the performance of general-purpose processors, the reliance on custom hardware accelerators to boost performance for specific workloads is growing. Heterogeneous integration at the system level with a combination of CPUs, GPUs, FPGAs and other accelerators can optimize system power and performance. SEMI: What solutions is Intel developing to address these market needs? Radhakrishnan: Intel is actively involved in the development of the industry ecosystem for heterogeneous integration. We have developed a number of innovative advanced packaging solutions such as the EMIB and Foveros that are used in products today. Intel is also developing the next generation of advanced packaging technologies, Foveros Omni and Foveros Direct, which will dramatically scale the IO density by using direct Cu-Cu bonding technology. Foveros Omni is a crucial building block technology to enable high-voltage power conversion on the package for efficient power delivery. Intel is uniquely positioned to predict the design needs for future systems and deploy its resources to develop the technology building blocks needed to continue performance scaling. Our IDM 2.0 strategy enables us to leverage our leadership in packaging technologies to design the best products and use the best IP to deliver leading products across a broad range of categories. SEMI: What do you expect from your participation at SEMI Connecting Heterogeneous Systems Summit? Radhakrishnan: I’m hoping to shed some light on some of the new technologies we have been developing at Intel to enable heterogeneous system integration. I also want to bring awareness to the power-related challenges we are facing with heterogeneous systems. I also look forward to listening to what other industry leaders have to say on the topic. Kaladhar Radhakrishnan is an Intel Fellow and a Power Delivery Architect with the Technology Development group at Intel. He plays a significant role in shaping and driving power delivery technologies for Intel microprocessors. His areas of expertise include integrated voltage regulators, advanced packaging and passives technologies. Kaladhar is a two-time recipient of the Intel Achievement Award, the highest Intel honor an individual or small team can receive. He has authored four book chapters, over 40 technical papers in peer-reviewed journals, and has been awarded 35 U.S. patents. He has also served as an adjunct professor at Arizona State University. Kaladhar joined Intel in 2000 soon after receiving his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign. Serena Brischetto is senior manager of marketing and communications at SEMI Europe.

Spend any time with Ansys’ John Lee, Rich Goldman or Marc Swinnen and you’ll hear plenty of optimism about the semiconductor industry even though they tick off a long list of looming design challenges. The need for reliable and effective electronic systems, they emphasize, is great and runs through high tech, aerospace and defense, automotive, IoT and 5G with communications being a common denominator. The three are especially bullish these days on changing market dynamics brought on by systems companies building company-specific bespoke, or custom, silicon. These systems companies are building chips with a different perspective and a fresh look at silicon design, a move away from the more traditional segment-specific silicon due to much more complexity. Ansys, a member of the ESD Alliance, a SEMI Technology Community, is a 4,100-employee company with a comprehensive portfolio of multiphysics engineering simulation software for product design, testing and operation products and services. John, Rich, Marc and I focused on Ansys’ semiconductor and electronics segment for our conversation. Smith: When did you notice the move by systems companies to build their own chips? What drives this trend? Lee: The inflection point was about three years ago when hyperscale data center and system companies recognized they needed an enterprise system design platform. They are designing bespoke silicon, driven to do this for cost efficiencies and to avoid relying on outside suppliers. They also want differentiation based on their specific platform needs so they can optimize compute power to their specific needs. Smith: What is driving the trend for multiphysics experience to ensure effective and reliable electronic systems? Lee: The increasing need for multiphysics analysis is acute. The physics of 3D IC, for example, brings in mechanical engineering with the convergence of mechanical and electrical as 3D emerges at the intersection of IC and System. As a result, physics becomes a necessity to analyze the stability of the chip in the package. Goldman: As well, the move to stacked chips, 3D IC and wafer-on-wafer requires thermal, electromagnetic and mechanical analysis in addition to the traditional analysis for function, performance and power. They all need to be analyzed together, not serially. It becomes multiphysics, not multiple physics. Smith: Two distinctly different disciplines – multiple physics and multiphysics – are needed for semiconductor design. How are they different? Why the need now? Swinnen: Multiple physics refers to the sheer breadth of physics that is now needed to analyze from the IC up to the largest system whereas multiphysics refers to the capability to analyze several physical effects concurrently, accounting for their impact on the design and interactions between various physics. Multiphysics are necessary to analyze the full context of the system environment – from nanometers to kilometers – for multi-chip packaging, chip-to-package-to-silicon and systems with multi-domain guidance. Goldman: A self-driving car, as an illustration, includes AI systems-on-chip, solid-state sensors, infotainment systems and radar/lidar detectors that must all work in the rain, the heat and the bitter cold. Smith: Why are design groups being reorganized to include expertise in mechanical and electromagnetic issues? Swinnen: Complexity has exploded, driven by a long list of technical requirements and, perhaps, mischaracterization. Goldman: Just consider the system on chip, mischaracterized by the semiconductor industry. The chip is never a system by itself. Rather, it is a complex component in a larger system and must be analyzed in that context. 3D IC is where this comes together and forces a recognition of physics outside the traditional scope of SoC design. 3D IC chips are much closer together on the board and it takes multiphysics embedded into the workflow of semiconductor design, packaging, system design and 3D IC to ensure they work reliably and efficiently. Smith: What is the solution? Goldman: It’s clear a specialized digital thread is necessary to move disparate groups with expertise in systems, physics and silicon together. Today, these groups or disciplines might not exist in the same company, whether it be a foundry, fabless or outsourced semiconductor assembly and test (OSAT) company. Lee: In order to unify the entire system design environment, a cloud-based, open and extensible heterogenous enterprise compute platform is required. It is similar to the SaaS-based business model and known as Simulation-as-a-Service (also SaaS). While vertical integration of design groups is already taking place at leading system design houses, there have also been advances in electronic design tools. These are starting to offer more comprehensive multiphysics capabilities including thermal, fluid dynamics (CFD), mechanical stress and reliability analysis in a single analysis cockpit. Today’s system designers face two platform challenges: First, they need an environment that is open enough to accept analysis results from multiple sources so that they can be overlapped and cross-analyzed. Second, the design platform must have the capacity to handle the enormous amounts of data generated by the latest 3-nanometer chips and 3D IC systems, and this implies an intimate coupling to elastic cloud computing. The days of an engineer writing Perl scripts and handing it off to someone else are gone. We believe that the industry is responding to this challenge with a new generation of design platforms that a cloud-native, open and extensible to allow heterogenous enterprise design. We are definitely at an inflection point in electronic design today, but the electronic industry has faced these before an we are confident it will master these challenges as well. About Rich Goldman Rich Goldman is director of marketing for the Electronics and Semiconductor Business Unit of Ansys. He holds a Bachelor of Science degree from Syracuse University and an MBA and Master of Science degree in Engineering Management. Moscow Institute of Electronic Technology (MIET)’s first honorary professor, he is also the recipient of honorary PhD degrees from Russian-Armenian (Slavnoic) University and State Engineering University of Armenia for contributions to the advancement of Armenia’s high-tech education and economic ecosystem. Rich served on EDAC’s board of directors. About John Lee John Lee is general manager and vice president of the Ansys Electronics and Semiconductor Business Unit. Lee co-founded and served as CEO of Gear Design Solutions (now Ansys), developer of the first purpose-built big data platform for integrated circuit design. He cofounded two other startups (Mojave Design and Performance Signal Integrity), which successfully exited into companies now part of Synopsys. He holds undergraduate and graduate degrees from Carnegie Mellon University. About Marc Swinnen Marc Swinnen is director of product marketing for the Electronics and Semiconductor Division of Ansys. He holds Master degrees in Electronic Engineering and Industrial Management from KU Leuven, Belgium, as well as an MBA from San Jose State University. About Bob Smith Robert (Bob) Smith is executive director of the ESD Alliance, a SEMI Technology Community. He is responsible for the management and operations of the ESD Alliance, an international association of companies providing goods and services throughout the semiconductor design ecosystem.

The state of manufacturing is changing rapidly. Regardless of sector or location, manufacturing decision-makers across the world are signaling a desire for better supply chain resiliency, manufacturing flexibility, increased speed of innovation and stronger environmental sustainability. Singapore’s manufacturing sector, a significant contributor to its gross domestic product, is always evolving and today is shifting away from its traditional focus on producing highly customized products using flexible manufacturing processes, but at significantly lower efficiencies. Today, with Industry 4.0, we can design manufacturing systems that optimize both efficiency and flexibility. And this is possible because of the convergence of technologies such as artificial intelligence (AI), data analytics, robotics and the Industrial Internet of Things (IIoT). This blend of technologies helps reduce the cost of technological solution ownership – a derivative of Right’s Law – as a function of cumulative production. In HP Singapore, driving innovation in our product and processes is part of our DNA, and over time our products have grown in complexity and breadth. We have embraced Fourth Industrial Revolution (4IR) technologies in our advanced manufacturing lines. We started our Industry 4.0 journey in 2016 with Vision and Mission 2020 to modernize our production facilities to smart factories that strengthen our competitive edge. Our focus was on upskilling our employees with future skill sets, build new technological capabilities and partner with higher education institutes. To drive these transformations, we have formulated five pillars: Additive Manufacturing Data Analytics Cyber-Physical Integration Digitalization Workforce Transformation These five pillars have enabled us to move from labor-intensive and reactive processes to processes that are highly digitized, automated, and AI-driven, enabling us not only to increase quality and productivity but also to reskill our people in anticipation of jobs they will need in the future. Technicians have been upskilled and promoted to techno-operators which has, in turn, freed up technical specialists to explore other roles. Engineers have retrained as data scientists, or have moved to new product development, for instance. In 2017, HP’s Ink Supplies Operations (ISO) set up Smart Manufacturing Applications and Research Centre (SMARC) to adopt 4IR technologies and implement these innovations in production lines. Today, SMARC is the home ground for HP engineers to experience, trial and prototype solutions, bringing innovative and sometimes unexpected solutions to manufacturing. It is also a showcase for industry partners, government agencies and schools. Here is how each pillar of the SMARC contributed to transformation to augment the manufacturing workforce: Cyber-Physical Integration – Move Role of robotics/automation – By standardizing automation standards for robotics, we have deployed collaborative robots (Cobots) and autonomous intelligent vehicles (AIVs) to perform manual and routine tasks to drive productivity, while reducing errors from operator fatigue and protecting our operators’ physical well-being. Digitalization – Sense Role of IIoT – Devices are a treasure trove of data that can provide clarity on how the entire manufacturing line is performing in real time. Building a platform that connects devices and collects data while allowing factory floor managers to dynamically visualize on an Integrated Command Centre (ICC) and manage factory performance is central to HP’s digital transformation journey. And IIoT is not restricted to just devices that are already wired for data sharing. HP has also connected off-the-shelf analogue devices using a standardized data transportation protocol, allowing HP to collect essential data across all types of devices and eliminating manual data entry. Additive Manufacturing – Build By embracing additive manufacturing (use of HP MultiJet Fusion 3D printers), HP introduced more flexibility in operations through on-site rapid prototyping, light production, and replacement of parts needed on our manufacturing floors, shortening production timelines. We 3D printed pallets, which are cheaper and faster to produce, and replaced original pallets for transportation on conveyor belts, improving the efficiency and productivity of our operators. Director Jamie Neo with HP’s MultiJet Additive Manufacturing Printer. (Photo Credit: HP) The HP Multi Jet Fusion 3D printing technology has helped HP to replace traditional manufacturing methods and streamline processes in our supply chain. For example, HP is 3D printing the Drill Extraction Shoe, a tool that is essential to the removal of waste products from laser-drilling in HP’s printhead manufacturing line. Through 3D printing, HP has consolidated the production of the tool from nine parts to one 3D printed model, thereby optimizing the design of the tool and reducing its production time from three to five days to 24 hours. Data Analytics – Think By deploying advanced analytics and machine learning models, HP has enabled real-time detection, diagnostics, and prediction of product quality across our manufacturing lines. Predictive models are replacing traditional “destructive testing,” reducing waste and allowing HP to meet unique product specifications more accurately. Machine learning is diagnosing and recommending the right set up for tools and manufacturing lines, when necessary, to reduce downtime and increase precision. Workforce Transformation – Grow The pivot to becoming an advanced manufacturing leader not only requires HP to invest in 4IR technologies but also skill sets to operate 4IR technologies. We embarked on a Workforce Transformation program to help our employees stay competitive in a fast-changing world. Today 35% of HP technical workforce have had the opportunity to take on new roles even as needs evolve, thanks to internal and external training and reskilling. Beyond technology and training, the glue that binds these together and makes it successful is our culture at HP. We are ambition-led, which means that we do not see the world as it is, but what we can be. And we do so by collaboration. Plans for the Future After accomplishing our Mission 2020, in late 2020 we launched Mission 2025 to extend our end-to-end smart factory capabilities through advanced connectivity, intelligence and automation to optimize and drive sustainable manufacturing flexibility and efficiency. Pyramid of HP’s smart manufacturing focus Advanced technologies such as additive manufacturing, IIoT, automation and robotics, data analytics, machine learning and AI are central to the connectivity and the end-to-end intelligence of our smart factories, enhancing production efficiency and flexibility while improving the quality of our products. For example, the deployment of IIoT sensors in our wafer plant has helped to reduce downtime in replacing CO2 gas cylinders. What’s more, AI enables us to more accurately monitor the dispensing of structural adhesive to eliminate lost yield. We believe that by enhancing manufacturing efficiency and flexibility, we were able to shorten resolution time, reduce our carbon footprint, and improve the resiliency of our manufacturing and supply chain systems. HP smart factory model In April 2021, two lines in HP Singapore joined the World Economic Forum’s Global Lighthouse Network after being recognized for pivoting from a labor-intensive factory into a digitized, automated one with the help of AI. In doing so, we managed to improve manufacturing costs by 20% and productivity by 70%. Under Mission 2020, we saw the following successes: Improved manufacturing costs by 20% Improved productivity by 70% Brought most HP employees onboard to our smart manufacturing journey Equipped HP employees with skill sets in areas such as additive manufacturing, data analytics, AI, robotics and Internet of Things Established a Model Factory playbook With Mission 2025, we will: Continue to train employees in future skillsets by partnering with institutes of higher learning Scale our Model Factory playbook across more manufacturing lines to reduce costs and improve productivity Enhance our knowledge in additive manufacturing by building an ecosystem as a service platform to help manufacturing companies Enable a sustainable manufacturing system to reduce our carbon footprint and help enable a circular economy We believe in innovating with purpose by focusing on solving real-world problems and creating technology in the service of humanity. That is why we built the SMARC to create the solutions for our lines and showcase these solutions to encourage industry participation. We are driven by values and ambition, which means that it is not just what we do, but also how we execute it. We make sure our values inform everything we do – for instance, helping us make a greater impact to environmental sustainability, people, and our community. We believe this is a crucial step in coalescing industry support, which is necessary to move the needle on advanced manufacturing. Robert Ronald is Master Program Manager, Cost Structure, Model Smart Factory and Sustainability, at HP.

In the span of a few short months earlier this year, Mentor Graphics became Siemens EDA and introduced a suite of integrated hardware-assisted verification tools, the first product launch under the new Siemens EDA brand. Jean-Marie Brunet, senior director of marketing, product management and product engineering at Siemens EDA, orchestrated the launch and connected with me for a discussion about the chip design verification space. As he pointed out, verification and validation of systems is a fast-growing and important market segment to the electronic system design ecosystem. Smith: What trends do you see in chip design? What is driving these trends? Brunet: Chip verification costs continue to grow faster than design costs because of factors such as increasing design complexity, rising computing power, surging I/O traffic activity, increasing energy consumption and the widespread use of peripherals. These dynamics are being driven by new data center networking, communications/5G, autonomous driving, artificial intelligence (AI) and machine learning (ML), and storage applications. These trends also indicate the need for more powerful verification tools and expanded verification objectives that include power and performance analysis. Hardware-assisted verification tools are perfect for meeting these demands. Smith: Chip design verification consumes the most time in a project cycle. Why is this so? Brunet: The verification of designs reaching multi-billion gates and supported by voluminous software stacks is fraught with challenges. To exhaustively check every possible state in a billion-gate design with simulation alone would require up to trillions of verification cycles. That’s why hardware-assisted verification is one of the fastest-growing technologies in EDA. Given the complexity of today’s SoC design, it’s no surprise that verification is the largest undertaking in the entire project design cycle, consuming more than 50% of it. It also has the greatest impact on quality, cost and schedule because it prevents designs from failing at first silicon. While a respin of a large design taped out at a node below 10 nanometers could cost more than $10 million, delaying delivery of a new product for a few months in a highly competitive market may cost hundreds of millions of dollars. Smith: What other challenges do engineers face trying to verify a chip design will work as intended? Brunet: Verifying an SoC design is a massive undertaking and, in parallel, verification teams are trying to streamline and optimize verification cycles. SoC design groups are tasked with completing full system-level verification prior to creating production masks by thoroughly vetting all hardware blocks, interactions between those blocks, and the software developed for the end application before the chip is built. To alleviate this enormous pressure, they are starting to adopt a shift-left methodology for early functional verification as soon as individual blocks of a SoC design become available. It helps jump-start embedded software validation before full system validation is completed to save time and allow engineers to work in parallel, not serially. While it is an effective approach, it creates the need for a complete and integrated suite of hardware-assisted verification tools to verify and validate a design’s hardware and software components. Smith: How do you define hardware-assisted verification and how does it help solve these challenges? Brunet: A typical definition of hardware-assisted verification is special purpose hardware to accelerate verification. In other words, hardware emulation and FPGA prototyping. Hardware-assisted verification is a mandatory investment as single-die or multi-die chips get larger with more complexity and more interfaces, making hardware and software code integration critical early in the design cycle. Because software performance defines a chip’s success, the need to perform software workload-based analysis is acute, not just analysis of chip functionality, but also accurate performance and power consumption in the context of real-world applications. Hardware-assisted verification is the only option when hardware and software meet. By combining emulation, desktop FPGA prototyping boards and enterprise FPGA prototyping platforms to work on the same SoC design, a verification group can assemble a complete hardware-assisted verification system for thorough and exhaustive verification and validation. Smith: Where are the big opportunities for hardware-assisted verification? Brunet: New end-user applications are coming from computing and storage, AI/ML, 5G, networking and automotive. Recently released market data from the ESD Alliance shows that in 2020, hardware-assisted verification revenues exceeded $700 million. It is reasonable to assume that revenues of $1 billion will be within reach in the next few years given the amount of chip design activity at advanced nodes below 10nm. Smith: With the design/verification and manufacturing phases of the semiconductor supply chain more closely aligning, what role does hardware-assisted verification play? Brunet: Semiconductor manufacturing and the supply chain that supports it benefits greatly from the continued innovation in verification and validation tools and methodologies. With this innovation, designs are delivered to the manufacturing flow with a much greater chance of passing first silicon with success. This reduces friction in the semiconductor supply chain since IP and chips are available when anticipated. Hardware-assisted verification is a quick-moving, highly leveraged resource that helps a design and verification team to ensure chips are manufacturable and meet the functionality, power and performance requirements for the end-product application. Jean-Marie Brunet is the senior director of product management and engineering for the Scalable Verification Solutions Division at Siemens EDA. He has served for over 20 years in application engineering, marketing, and management roles in the EDA industry, and has held IC design and design management positions at STMicroelectronics, Cadence, and Micron, among other companies. Jean-Marie holds a Master's degree in Electrical Engineering from I.S.E.N Electronic Engineering School in Lille, France. Jean-Marie Brunet can be reached at [email protected]. About Bob Smith Robert (Bob) Smith is executive director of the ESD Alliance, a SEMI Technology Community. He is responsible for the management and operations of the ESD Alliance, an international association of companies providing goods and services throughout the semiconductor design ecosystem.