Technology and Trends

John Kibarian, CEO and founder of PDF Solutions and a member of the ESD Alliance (ESDA) Governing Council, is a keen observer of the semiconductor ecosystem. Since PDF Solutions sits between design and manufacturing, Kibarian shared unique perspectives on both in a recent discussion.Smith: What trends are you seeing in the semiconductor industry. Are there any that surprise you? Kibarian: We see several trends that have been going on for quite a while.As much as we hear Moore’s Law is dead, there's still a strong drive to get to advanced nodes. The benefits are harder to achieve and require more than geometry scaling, but demand for these advanced nodes continues to grow. Another emerging trend is the need for insatiable compute power in data centers to support the explosion in AI applications. In recent history, the mobile phone market has been the key driver of the push to new advanced nodes, but that is changing as the performance needs of data centers and AI applications are now driving the shift.Next, as companies are still learning from the disruptions in the supply chain due to the pandemic, there’s a tremendous amount of movement to make the supply chain more resilient by expanding sourcing options for critical products or test applications. This is happening in conjunction with significant investment in high-performance compute from many countries that want to bring silicon to their shores.The next trend is that electronics companies are looking to limit investing solely in China or the U.S. Their China Plus One or U.S. Plus One strategies results in adding significant additional infrastructure and overhead. If it's not done right, it will cost the industry more money. It will be hard to sustain the cost benefits and economies of scale of the current single source model just by brute force and adding human capital. A new approach is required to manage cost effectively smaller and globally distributed manufacturing facilities.The final trend is the general electrification of the economy. Cars are moving from internal combustion engines to electric. That means more and more of our energy needs are met with electricity, putting a premium on solar and batteries. Batteries require power conversion.Silicon such as high bandwidth semiconductors on silicon carbide and gallium nitride have a tremendous amount of capacity. What is interesting is how fast and aggressive China is in that part of the market; they could be a major producer of the technologies needed to support electrification. With our exposure to the China market as well as the European and U.S. markets, Chinese manufacturers have come up quickly, and we may see a world with more viable suppliers than originally anticipated.Smith: You mentioned data centers and AI. AI is everywhere and revolutionizing the semiconductor industry. EDA companies are talking about incorporating AI. What are you observing? Kibarian: AI is used for chips that are manufactured for use in data centers. For example, our customers use PDF analytics or the Exensio platform via the cloud to analyze large amount of manufacturing data and product or test engineering data. Without this type of automated solution, only a small proportion of these data sets would actually be utilized.Companies staff their product design and test engineering using a budget based on a percentage of revenue. If a company has billions of dollars of revenue, it will put so much more into product and test engineering. But how productive can these people be? Without AI, they can only use some simple reports and graphics to analyze the subset of data they are looking at. AI solutions such as PDF’s Guided Analytics capability apply sophisticated machine learning tools to analyze entire large data sets. AI is enabling engineers to be more productive by allowing them to work with large data sets that ultimately deliver better results in the products.The amount of compute keeps going up at a rate that outpaced the rate of geometric scaling. More compute power makes it cost effective to go through large data sets and identify what is relevant.Additionally, AI is helping semiconductor companies build products. A conventional compute system is chips assembled on boards. AI is making system-in-package take off.The production flow is more complex, as fabless companies are becoming system companies. Conversely, system companies are becoming fabless companies and manufacturers. In the past, they ordered parts from their foundry of choice. Essentially, the foundry was the system manufacturer, supplying package and test yields of 99%.Now companies are building systems in more complex packages potentially with foundry partners, but this requires getting known good die. High bandwidth memory or other components from other suppliers means the company must make sure these products are available at the right time. In essence, they are becoming manufacturers and changing the way customers manage the problem of product test. They're adding more test insertion points and using machine learning and AI to be more productive.Smith: Let’s talk about digital twins or creating virtual models of everything from chips to the whole system. How do you see the impact or effectiveness of digital twins in manufacturing? Kibarian: From a manufacturing perspective, digital twins had been models for chamber behavior on a processing tool like an etch tool or TCAD simulation of devices and structures.The problem is that purely physics-based digital twins don't exist, and we must utilize empirical data. The joke was that the modeling for tomorrow’s systems was based on yesterday's technology. Trying to have the physics catch up with the materials, device structures and behaviors is why it’s so expensive to develop new technology.Principles-based models will never catch up with production. We can model 90-nanometer technology, but it doesn’t work for one or two nanometer wafers. AI and machine learning – and ways of building models using more sophisticated algorithms – can help close that chasm, and that’s starting to happen at the R D level.In production, no one has yet achieved a good merger of the physics-based and AI-modeling worlds to create a virtual model. Virtual modeling is a big opportunity.The rate of change and improvement in algorithms in large language models moves fast because machine learning can scrape the Internet for data to build huge training sets. In the semiconductor world, however, data sources are typically siloed within organizations and often not shared with vendors. This limits the rate at which the industry can take full advantage of existing data and create tangible economic benefit.By and large, there is a lot of wasted capacity in semiconductor manufacturing. The operational effectiveness of factory equipment is up to 90-95%. The reality is that most factories today process product wafers 40-60% of the time – maybe 70-75% of the time on a test floor. It is critical for the industry to start leveraging new types of AI models to increase the productivity of its manufacturing capacity.The industry needs to look at how companies can share data to take advantage of more sophisticated AI and create a new kind of operational digital twins. If the industry doesn't make a change; it will only be the largest facilities with the largest datasets able to take advantage, leaving one or two winners, with the others not being competitive.Smith: Is it possible for the industry to come up with a standard or some way of sharing information to build better models without giving away the underlying proprietary data? Kibarian: We can look at computer science with technology like homomorphic encryption. The relationships between parameters remain, but the underlying numbers or raw data is not visible after encryption. Pharma and the medical industry have ways to add noise to the data while preserving the information, as required by the Health Insurance Portability and Accountability Act (HIPAA).Our industry has a knee jerk reaction when it comes to looking at how to take full advantage of data and prefers to solve it as if information and data is more proprietary than medical data or financial data. And I don't think that’s true.Bob Smith: Is the open-source movement destined to bring change to the industry? Kibarian: PDF is a big believer in open source when it comes to OS-level virtualization and Kubernetes versus proprietary alternatives. We also use open-source database technology like Cassandra but are skeptical of the value of open-source solutions for end-market verticals. Having an underlying open and available IT layer has tremendous value, because it means a more rapid rate of innovation and greater ability to adjust security vulnerabilities and patches versus proprietary systems.Smith: PDF sits right between manufacturing and design. On the EDA side, more collaboration is going on between designers and manufacturing. How would you bring these two domains closer together? Kibarian: That's a good question. My first instinct is to look at the largest design organizations and manufacturers. They often invest heavily to figure out how to get jobs done right. This results in the concentration of the industry on a smaller number of players and leads to less innovation. However, in the world of chiplets and advanced packaging, there are more opportunities to become a chiplet supplier, because the whole system doesn’t need to be built by a single company. A supplier of chiplets could sell it into many systemsFrom a system view, connecting the pieces together through software, data sharing and analytics could drive more productivity gains that will offset some of the natural headwinds. This needs to be addressed in a way that changes the paradigm with software and systems used to bring manufacturing and design closer together.About John KibarianJohn K. Kibarian is President, Chief Executive Officer and Co-Founder of PDF Solutions. He has served as President since 1991 and CEO since 2000. Dr. Kibarian received a Bachelor of Science degree in Electrical Engineering, a Master of Science and PhD degrees in Engineering Computer Science from Carnegie Mellon University.Robert (Bob) Smith is Executive Director of the ESD Alliance, a SEMI Technology Community.

In recent years, SEMI has been increasing its attention to sustainability within the global semiconductor industry. With the formation of the Semiconductor Climate Consortium (SCC) within the SEMI Sustainability Initiative, member companies have joined hands to collaborate and collectively tackle the challenge of meeting the industry’s ambitious sustainability goals.The SEMI Smart Manufacturing Initiative is a global effort focused on the future of manufacturing in electronics that helps microelectronics manufacturers enable business value by facilitating awareness and problem-solving to reduce barriers and realize timely ROI for critical Industry 4.0/5.0 technology deployment.Addressing the Industry’s Sustainability Inflection Point The Initiative’s Accelerating Sustainability with Smart Manufacturing Task Force formed in the summer of 2023 is a pivotal effort that collaborates with the SEMI Sustainability Initiative. The task force provides the “how” to the “what” of corporate sustainability goals, focusing on a bottom-up approach that leverages various sensing technologies, at the cleanroom, sub-fab and facilities levels for both greenfield and brownfield device-making facilities, to enable predictive analytics.The task force is designed to be a driving force to encourage the industry’s shift from a fragmented, top-down approach – which does not address the increasing usage of carbon as device-making becomes more complex – to a more integrated, bottom-up strategy. The task force has created a roadmap based on three pillars comprising connecting, sensing, and predicting technologies, which are meant to be cumulative and could dramatically reduce the carbon footprint for making microelectronics like semiconductors. The task force participants believe this to be the first industry roadmap to address all areas of fab processing, leveraging Industry 4.0/5.0, including AI for the purpose of enhancing the sustainability of operations.The task force’s roadmap is comprehensive and includes a variety of sensing technologies, digital twin methodologies, and machine learning/AI techniques for reducing Scope 1 (direct, process-based) and Scope 2 (indirect, energy-related) greenhouse gas (GHG) emissions, water usage and material waste. Once the roadmap is completed, factory management can prioritize the implementation of best practices based on the quantified impact factors of specified use cases. The roadmap outlines methodologies for brownfield and greenfield fabs separately based on practical capex and implementation recommendations.The task force has finalized its assessment of practical solutions for reducing Scope 1 and Scope 2 emissions, and it is now addressing water usage best practices before addressing material waste later this year and into 2025. SEMI will publish separate white papers regarding Scope 1 and 2 as a precursor to offering the roadmap as a customized tool for fabs.The semiconductor manufacturing industry stands at an inflection point from a sustainability perspective. Consider this: U.S. CHIPS Act funding is set to support 19 greenfield fabs, which collectively will consume the equivalent of 11 Empire State Buildings’ worth of steel. That’s a monumental environmental impact. Additionally, the Q2 2024 SEMI World Fab Forecast is tracking approximately 104 new fabs worldwide coming online between 2023 and 2027. Each of these fabs requires significant amounts of concrete, steel, and construction equipment, all with their own GHG footprints. This all represents a challenge that the industry must grapple with head-on.Furthermore, there is the energy-intensive nature of large semiconductor fabs, the cost implications of renewable power acquisition, and the substantial water usage by medium and mega-sized fabs. The main mission of the task force is to create an industry roadmap—a practical blueprint—for device makers to invest in sustainability, following a systematic, bottom-up approach.Accelerating Sustainability with Smart Manufacturing at SEMICON West At the Smart Manufacturing Pavilion at SEMICON WEST 2024 , a special session centered around the key components of the Accelerating Sustainability with Smart Manufacturing Task Force roadmap. Task force leaders helped coordinate a unique session to showcase the roadmap findings and detailed case studies for Scope 1 and 2 emissions. The session included speakers from Deloitte, Linkan Engineering, Micron, Solvay, Spectrum Environmental Services, and ULVAC.Accelerating Sustainability with Smart Manufacturing Session at SEMICON West 2024The opening keynote by Adeline Tay of Micron covered the company’s pioneering efforts to improve sustainability in semiconductor manufacturing. Tay highlighted how to enable a net-zero transition sooner via smart manufacturing technologies including digital twins, real-time power monitoring and optimization, lower global warming potential gas usage, and emission data visibility.The next presentation by Brian Coppa (Task Force Co-Chair) of ULVAC [1] revealed the findings of the SEMI task force roadmap, which described the most critical technologies to reduce Scope 1 and 2 emissions and quantified them with respect to impact level to help fab managers prioritize budget allocation for meeting related sustainability goals at the cleanroom, sub-fab and facilities levels. Coppa provided estimates on the substantial decreases in emissions that can be achieved (see Figure 1) using the compilation of recommended technologies (i.e. predictive analytics such as AI, machine learning and predictive maintenance) that are outlined in the task force roadmap. Figure 1: Task force roadmap estimates for the emissions reduction potential for Scope 1 and 2, respectively.Jake Townsend of Deloitte [2] followed and underlined the industry’s sustainability challenge with regards to chip-making carbon footprint and the opportunities from design to fab, to sub-fab and facilities. Townsend presented that the industry’s water management carbon footprint is estimated to be at least 2 orders of magnitude higher by weight compared to consumer products such as a car or hamburger.Next, Steve Hall of Spectrum Environmental Solutions [3] discussed how compliance testing and measurements differ from common GHG reporting, which is calculation based. The Electronics Code for Federal Regulations 40CFR 98 Subpart 1 now requires the electronics industry to report both calculated emission factors and smokestack measurements. Hall showed the benefits of Fourier-transform infrared spectroscopy (FTIR) monitoring at the smokestack level and the accuracy it can provide for reporting.Michael Peter Pitroff of Solvay [4] provided an in-depth case study on how the F2-based cleaning gas Solvaclean® can replace SF6 chemistries for chamber cleans, facilitated by in-situ gas monitoring techniques, to reduce Scope 1 emissions in plasma etch and deposition processes. Pitroff covered the financial and throughput impacts of the replacement and described how higher global warming potential (GWP) gases can be replaced with low GWP gases in Bosch wafer etch processes.Finally, Drew Horseman of Linkan Engineering [5] presented innovations in water treatment for the semiconductor industry. Horseman covered current state-of-the-art, energy-efficient reverse osmosis (RO) systems and addressed specific benefits of RO optimization. He showed how data acquisition and analytics are the first step to eventually reaching zero-liquid-discharge (ZLD), which is the future of sustainable water treatment.Get Involved!Overall, the Smart Sustainability session at the Smart Manufacturing Pavilion at SEMICON West 2024 enlightened many attendees on how smart manufacturing technologies like AI can help advance sustainability in semiconductor manufacturing using a more scalable, automated approach. Visit the SEMI Smart Manufacturing Initiative homepage to learn more about upcoming activities and contact [email protected] to get involved in the task force.Amit Srivastava is Manager, Data Science- Smart Manufacturing and Artificial Intelligence at Micron Technology, Brian Coppa is Product Engineering Lead at ULVAC, and they lead the SEMI Accelerating Sustainability with Smart Manufacturing Task Force. Mark da Silva is Senior Director of the Smart Manufacturing Initiative and APHI Technology Community at SEMI. Topics: Smart Manufacturing , SEMI Smart Manufacturing Initiative , semiconductor manufacturing , Digital Twin standards , manufacturing productivity , Digital Twin framework , manufacturing efficiency , semiconductor industry , semiconductor ecosystem, sustainability , SEMICON West , decarbonization , Semiconductor Climate Consortium , greenhouse gas emissionsReferences from the Smart Sustainability Session at Smart Manufacturing Pavilion available to SEMICON West 2024 attendees: SEMI Smart Sustainability Roadmap: Blueprint for Device MakersGreen Chips are the New Blue-Chip InvestmentMeeting Net Zero Manufacturing Challenges with Real-Time Monitoring of Exhaust Laterals in Sub-FabsSolvaClean as replacement of SF6 in cleaning and etchingInnovations in Water Treatment: Exhibiting the Importance of Data in Sustainable Process Development

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