Artificial Intelligence

The increasing complexity of IC manufacturing poses a number of challenges including the mask data preparation required to enable technology improvements such as multi-beam (MB) mask writers and extreme ultraviolet (EUV) lithography. Thiago Figueiro, VP of Sales & Marketing of Aselta weighs in.

A steel mill and a 200mm semiconductor fab face a similar challenge: How to improve output, whether in Tons Per Year (TPY) or Wafer Starts Per Month (WSPM), while saddled with a capital equipment footprint that cannot be readily replaced or easily expanded.

Azeez Bhavnagarwala, Metis’ founder and CEO, speaks about advanced CMOS Memory and Arithmetic component intellectual property (IP) to improve energy efficiency and performance of processors.

Anna Fontanelli, Monozukuri’s founder and CEO, is an expert in deep-submicron silicon technology and design tools shares her view on the state of Moore’s Law and challenges surrounding 2.5D integration, 3D chip stacking & advanced packaging, as well as the chip design industry’s startup environment.

During their session Creating a Data Ecosystem to Enable End-to-End Advanced Predictive Manufacturing, Laura Matz of EMD & Raj Narasimhan of Micron provided a case study of their uncommon partnership in creating a shared data ecosystem to prove the advantages of advanced predictive manufacturing.

Dr. Lanza, Managing Partner of Lanza techVentures and 2014 winner of the Phil Kaufman Award for Distinguished Contributions to Electronic System Design, addresses why he believes semiconductors in MedTech is about to become the next big thing.

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.

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.

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.