Industry 4.0

Making Strides TogetherKnowledge is power – especially when it is shared. This principle formed the foundation for Micron’s Go and See virtual visit of its Singapore manufacturing plant on 26 August 2020 as 27 companies including GLOBALFOUNDRIES, ST Microelectronics, Infineon, TEL, ViTrox , IBM, HP and UTAC joined the first-of-a-kind virtual factory visit. The chip industry powerhouses gathered to see how Micron’s Lighthouse frontend wafer fabrication facility leverages Fourth Industrial Revolution technologies to drive new production and cost efficiencies.They saw clear markers of a transformed organisation and spoke with working-level staff, managers and front-line employees. Company representatives also met virtually with Micron management teams from organisations that led its digital transformation – from pilot programs to integration at scale – to realise significant financial and operational benefits. The mix of technologies they deployed to make it all happen included artificial intelligence (AI), big data analytics and the Industrial Internet-of-Things (IIoT).Micron’s Singapore-based fab facility earned Lighthouse certification earlier this year from the World Economic Forum’s Global Lighthouse Network. The Go and See tour was co-sponsored by SEMI Southeast Asia and McKinsey Company.Transformation is CrucialBy embracing Lighthouse principles, semiconductor sectors and companies can accelerate their digital transformation to boost operational and financial efficiency while helping increase productivity across the electronics supply chain. It will take time for Southeast Asia semiconductor manufacturers to transform to digital operations, though we’re seeing growing interest in Industry 4.0 practices as they begin to understand that the deployment of new technologies and applications will help them better understand real-world benefits of smart manufacturing use cases and solutions. SEMI believes shining the spotlight on companies like Micron can illuminate the way forward for other companies to help drive the industry’s digital transformation. We look forward to seeing companies build on this momentum as they start to leverage leading-edge technologies to improve efficiencies and promote sustainability.Bee Bee Ng is president of SEMI Southeast Asia.

Inertial sensors have continued to underpin the success of wearables in increasingly important ways. Propelled by evolutionary advancements in inertial sensors, wearables have strayed from their humble beginnings in simple activity and wellness, which defined the user experience over the past decade. What started with the simple act of telling people their daily step count has morphed to provide deeper insights into swim stroke and run cadence, all the way to mapping out a person’s off-piste ski route. Layered on top of this foundation of inertial sensors, we’ve fused optical, temperature and other sensor technology to provide clinical-grade healthcare snapshots available previously only by visiting the doctor’s office.Inertial sensors today are again leading the way in improving health and wellness. Instead of humans, however, this time the patients are machines. In fact, the health of critical assets – whether factory-based equipment, windmills, train bogies or aircraft – has been assessed through sophisticated analysis of their vibration signatures for many years. The sensors used for these applications have depended on piezoelectric technology because their vibration amplitude signals are very small and difficult to detect and because of the importance of understanding their spectral content over a wide bandwidth. When it comes to noise and bandwidth, bulk piezoceramics have had a major advantage over electrostatic MEMS technology – until recently.Using bulky expensive piezoelectric sensors for condition-based monitoring has been akin to going to the doctor’s office to have an MRI. The equipment required (sensors, receivers) is expensive and requires highly trained specialists to operate the machine and to interpret the information. For this reason, only mission-critical assets are instrumented. For nearly all other equipment, we tend to use inefficient schedule-based maintenance approaches to cover the gap of not having continuous data. Condition-based monitoring leverages real-time sensing of critical machine parameters to reduce system downtime and improve efficiency. Evolving machine healthMEMS started to democratize machine health several years ago, when suppliers began switching from piezoelectrics to capacitive MEMS. While the performance was still not on par with piezoelectric sensors, MEMS technology could already capture a wide array of faults. One example, the ADXL001, started making its way into Integrated Electronics Piezo-Electric (IEPE) and 4-20 mA sensors, which form the backbone of the vibration monitoring market. Although the bandwidth and noise of the sensor did not allow for very early detection and prescriptive monitoring, it did allow the tracking of faults as they progressed and became more imminent.Other digital accelerometers started finding their way into new wireless prototype systems with the goal to simplify and increase deployment to a greater population of assets. The thinking was that self-contained digital wireless sensor nodes could be deployed more economically and quickly, and that these digital sensors would bring the power of computing to the edge node.Unfortunately, even the lowest-noise MEMS products did not have the bandwidth needed to diagnose and predict faults early enough to influence how and when machines are maintained most economically. Instead, such devices were used to detect imminent failure to prevent irreparable harm. As we all know, however, the earlier the doctor spots a problem, the better the probable outcome. That’s because early detection increases the likelihood that the doctor will have access to the full spectrum of treatment options available to fix the problem.Inertial MEMS is blazing a new frontier with the introduction of next-generation capacitive MEMS such as the ADXL100x portfolio. Offering ultra-low noise density and high-frequency response, these newer capacitive MEMS devices fit the bill. With 3dB bandwidths up to 25 kHz and flat response curves within 0.4dB all the way to 10kHz, these accelerometers demonstrate compelling enabling characteristics such as better DC performance, improved robustness, lifetime stability, linearity, and of course, cost, making capacitive MEMS a better choice than piezoelectrics.With high-bandwidth capacitive MEMS much easier to use and deploy – as well as more affordable – the market is starting to respond. Condition monitoring equipment and instrumentation is becoming more accessible to a larger base of manufacturers. In turn, a wealth of data is being created and mined to develop better and timelier predictive and prescriptive maintenance approaches that rely heavily on machine learning and artificial intelligence (AI).It’s worth paying attention to the sizable condition-based monitoring market. Estimated at $3.5 billion and growing, condition-based monitoring reduces downtime and increases equipment utilization in quantifiable ways. And it’s not just manufacturers who stand to benefit. More sustainable and efficient industrial processes, safer trains that crisscross continents at ever increasing speeds, autonomous cars and trucks that know what’s happening under the hood as well as on the road, and modern infrastructure to support our evolving lives show us that condition-based monitoring has something for everyone.Learn more about Analog Devices’ condition-based monitoring signal-chain options that help customers on the journey from sensor to solution. View ADI’s whole portfolio of condition-based monitoring solutions online or download Next-Generation Condition-Based Monitoring brochure.Tzeno Galchev is product marketing manager in the Inertial Sensor Technology Group at Analog Devices Inc. He oversees the strategic marketing and product definition of the inertial sensor component portfolio. He received B.S. degrees in both Electrical and Computer Engineering in 2004, and M.S. and Ph.D. degrees in Electrical Engineering in 2006 and 2010 respectively from the University of Michigan, Ann Arbor. He has over 30 publications in the area of MEMS, holds multiple patents, and is a frequent lecturer and speaker on topics related to MEMS, energy harvesting and sensors.Analog Devices is a longtime member of MEMS Sensors Industry Group (MSIG), a SEMI technology community that enables the MEMS and sensor industry to address common challenges, innovate and accelerate business results.

Semiconductor companies that begin revising their long-term strategies now may emerge stronger in the next normal.In the months after the coronavirus began to spread, semiconductor companies moved decisively to protect employees, secure supply chains, and address other pressing concerns. Although the situation is still serious and many governments are still imposing physical-distancing requirements, semiconductor leaders are now looking ahead to the time when the pandemic abates and the next normal begins. To prepare for that moment, they are thinking about strategies for reimagining and reforming their business models—two activities that McKinsey described in a framework for responding to the coronavirus.Every aspect of the business model could be subject to change, including the composition of product portfolios, capital expenditures (capex), R D strategy, demand forecasts, supply-chain footprints, production decisions, and options for mergers and acquisitions (M A). But with so much uncertainty ahead, semiconductor companies may have difficulty making strategic decisions. To move forward, they should first establish a solid baseline for their company (see sidebar, “Determining the starting point,” for more information on this topic). With this foundation, semiconductor companies can chart a path to the next normal by focusing on the following questions: What recovery scenarios are most likely, considering evolving demand, economic developments, and other global changes? What is the impact of the COVID-19 crisis on long-term trends and demand? How can we emerge even stronger from the crisis? In past downturns, companies that thought about strategic questions early in the crisis were most likely to recover quickly and become market leaders. Although the COVID-19 pandemic is unprecedented in modern times, the need for long-term planning still holds true.Developing recovery scenariosCOVID-19 has significantly altered the fundamentals of the sector, including customer behavior, business revenues, and numerous aspects of corporate operations. Many companies have unclear future prospects, and some may not survive the crisis. Multiple recovery scenarios are possible, depending on potential government interventions and other variables that are now difficult to predict.Earlier, we published an article about the short- to medium-term outlook for semiconductor demand. Our analysis was partly based on assumptions in two of the nine scenarios that McKinsey developed for the COVID-19 recovery, both of which assume that the spread of the coronavirus is eventually controlled and catastrophic economic damage is avoided. In the first scenario, termed A3, global gross domestic product (GDP) recovers in the fourth quarter of 2020; in the second, termed A1, recovery is delayed until late 2022. Since the original analysis, we have updated the estimates to include 2021 demand.Both recovery scenarios suggest most semiconductor segments will experience negative year-on-year revenue growth in 2020.Both recovery scenarios suggest that most semiconductor segments will experience negative year-on-year revenue growth in 2020. Looking ahead to 2021, however, we expect that the situation will improve as most end markets recover, mostly because the starting point for 2020 will be much lower than it was in previous years. In the more optimistic A3 scenario, only a few segments meet the growth expectations that were forecast before COVID-19 emerged by 2021 (Exhibit 1). In the more pessimistic A1 scenario, the number of segments that recover is even lower (Exhibit 2). Within the individual segments, a few trends stand out: PCs. This segment will see the sharpest drop in demand and the performance gap will become more serious over time. Most people will buy all the home-office electronics that they need for remote work in 2020, lowering demand for next year. Meanwhile, enterprises may continue to delay investments in PCs to control expenditures, even if the recovery is proceeding. Automotive. In the more optimistic recovery scenario, A3, the automotive segment sees year-on-year growth of 28 to 36 percent in 2021. This estimate is based on the assumption that governments will offer incentives to car buyers. In A1, the scenario with the delayed recovery, government incentives are not as strong and growth remains in the 1 to 5 percent range. Wired communication. Growth in this segment could exceed pre-COVID-19 forecasts in both 2020 and 2021. This is one of the few areas where a delayed recovery would actually contribute to higher growth than the more optimistic scenario, since continued remote work and homeschooling will stimulate demand for wired communication. Evaluating the impact of the COVID-19 crisis on long-term demandBeyond 2021, semiconductor companies may have more difficulty predicting demand because even greater uncertainty abounds about healthcare and business developments. As companies create long-term plans and evaluate potential scenarios, trends in two areas deserve particular attention.Market pullOver the past few months, people around the world have experimented with new ways of working, studying, and communicating through videoconferencing and other technologies. Such trends could have a lasting impact on semiconductor demand and open new possibilities for existing products and services. For example, demand could increase for semiconductors that enable servers, connectivity, and cloud usage as online collaboration grows. Semiconductors may also be in high demand for the following products and services: contactless solutions, including touch screens and elevator buttons ambient assisted-living devices, including sensors, that help elderly and chronically ill patients remain in their homes, rather than moving to facilities automated-delivery solutions for the last mile, such as robots and drones digital work processes and the Internet of Things, especially in lagging sectors, such as healthcare, government, and defense Of course, COVID-19 could also decrease semiconductor demand in several important areas. Some automotive makers have already begun to postpone investment in autonomous driving because their lower revenues meant that less funding is available for R D. In other areas, demand trends are difficult to predict. Looking again at mobility, it is clear that public transportation is now less popular because people fear viral transmission. If subway and bus ridership remains low, or if more people begin to purchase private cars, semiconductor demand could shift in response.Monitoring industry shifts and geopolitical responsesOn the supply side, the pandemic has exposed risks that were previously unrecognized, leading to potential shortages of critical parts and components. In response, many semiconductor companies are already reconfiguring their supply chains to improve resiliency, and the changes may continue into the next normal. As they plan ahead, semiconductor companies might want to create scenarios that show the potential impact of localizing production, increasing stock and inventory levels, or making other changes.Within plants, the COVID-19 crisis could accelerate automation and the adoption of Industry 4.0 technologies. Remote manufacturing, diagnostics, and maintenance could all become permanent features. If that occurs, semiconductor companies might become smart workspaces, with technologies that facilitate remote work for most employees. They might also encourage a hybrid model in which a certain number of employees are remote and the rest remain on site. The efficiencies gained through such changes, as well as their start-up costs, could influence future semiconductor revenues.Long-term scenario planning must also consider the geopolitical response to the COVID-19 crisis. To stimulate the local economy, several governments have already announced subsidies and incentives, but these often vary by region. China for example has announced extended state subsidies and tax breaks for consumers purchasing new electric vehicles, while the United States has reduced fuel-efficiency standards for automakers. Semiconductor companies should closely track such regional variations, since they may affect demand patterns, and note whether local government responses appear to be evolving.Emerging stronger from the crisisSemiconductor companies have developed effective crisis-management strategies during other difficult periods, including the dot-com bubble in 2000 and the Great Recession of 2008. But the COVID-19 crisis presents entirely new challenges that make it different from any previous downturn. It hit unexpectedly and has exacted an immense humanitarian toll in addition to causing economic hardship. Although no playbook exists for such a crisis, some lessons from past downturns may apply if semiconductor players want to emerge stronger in the next normal.Modestly reducing capital expendituresIntel’s cofounder, Gordon Moore, once observed, “You can’t save your way out of a recession.” Large capex reductions are unavoidable if companies need greater liquidity to survive a crisis. But for companies in a better financial position, experience suggests that enormous cuts may not be the best strategy. During the Great Recession, many of today’s leading companies reduced capex less than their competitors and thus were better positioned to prepare for growth once the economy began to recover. With the current crisis, companies that proceed with plans to create next-generation products, purchase equipment, or make similar investments will be prepared if demand surges as the economy recovers. Those that hold back may have difficulty catching up, since some improvements can take years.Focusing R D budgets on next-generation productsFor maintaining a strong R D strategy during a crisis, three actions can be critical: Limiting cuts to R D budgets. As with capex, research shows that top companies tend to make moderate R D cuts during a downturn, allowing them to sustain a rich and evolving product portfolio. Unless liquidity issues require more significant cuts, companies should strive to fund innovation, rather than setting the bare minimum budget needed to keep R D running. Those companies that retain their focus on R D innovation now could gain long-term advantage over competitors, given the often lengthy timelines for developing new products. In some cases, the lagging competitors may never close the innovation gap. Focusing on next-generation products. Although semiconductor customers might be limiting their spending now, demand for new and innovative products could surge once the economy begins to recover. Rather than simply improving products using current state-of-the art technology, companies should also invest in next-generation products using new technologies. They may not generate revenue from these products over the next 12 to 24 months, but they will be well positioned once customer demand surges. Keeping a close eye on trends. Forward-thinking semiconductor companies will try to determine what products will generate the highest demand post-COVID-19 and prioritize their R D investments accordingly. Their analysis should encompass all areas, from new manufacturing techniques that allow for smaller process sizes to more innovative sensors. To make the right decisions, semiconductor companies must closely monitor new trends and customer behavior. If unexpected market shifts occur, they may need to take a new course. Taking a strategic approach to mergers and acquisitionsSemiconductor companies may also emerge stronger from the COVID-19 crisis if they take a strategic, systematic approach to investment and divestment. A retrospective, cross-industry analysis of 1,000 businesses shows that today’s top 100 companies were 10 percent more likely to undertake programmatic M A—the regular pursuit of modestly sized deals—both during and after the Great Recession (Exhibit 3). For divestment, the top 100 companies also unloaded 1.5 times more assets than their peers during the downturn. Another striking finding: the top companies also were more likely to pursue smaller deals. Overall, their average deal value was about 9 percent lower than that of competitors.A programmatic approach to M A is well-suited to the current era, since governments may implement stricter controls on large deals to limit foreign investment. It is possible that some protections may even extend to smaller deals to protect local businesses from hostile takeovers by international companies, so semiconductor players must examine regional regulations closely before proceeding with any M A activity.The world will be a different place after the COVID-19 crisis, and we do not yet know the extent of the changes within business, healthcare, and society as a whole. With so much uncertainty ahead, semiconductor companies will benefit by creating multiple future scenarios, each showing different macroeconomic and virus-related outcomes, as they set their strategy for coming years. They should embrace the uncertainty as part of their operating model, since agility and the ability to adapt quickly will be far more important than sticking to a plan. As in previous downturns, those semiconductor companies that act quickly could emerge stronger. Modest capex cuts, a focus on R D innovation, and a programmatic approach to M A could help them capture growth and create leading-edge technologies that will be in high demand once the economy begins to recover.About the authorsHarald Bauer is a senior partner in McKinsey’s Frankfurt office, Ondrej Burkacky is a partner in the Munich office, Peter Kenevan is a senior partner in the Tokyo office, Abhijit Mahindroo is a partner in the Southern California office, and Mark Patel is a senior partner in the San Francisco office.The authors wish to thank Daniel Anger, Stefan Burghardt, Sungwoo Chung, Viktoria Medvedenko, Sebastian Peick, Klaus Pototzky, Larissa Rott, Luisa Russwurm-Bössinger, and Klaus Seywald for their contributions to this article.Republished with permission from McKinsey Company.

The evolution of industrial and non-industrial automation, smart manufacturing, and Industry 4.0 technologies have increased demand for vision systems that support robust, reliable imaging industrial applications. What factors are driving growth in the machine vision market today?SEMI spoke with Frederic Laune, Business Manager, European Technology, Corning, about how Corning® Varioptic® Lenses are vital to advancing the speed, efficiency, and integration of products using computer imaging. Laune shared his views ahead of his presentation at SEMI MEMS Imaging Sensors Summit, 25-27 September, 2019, at the WTC in Grenoble, France. Join us at the event to meet Corning and many other key industry influencing players. Registration is open.SEMI: Corning's markets include optical communications, mobile consumer electronics, display technology, automotive, and life sciences vessels. Back in June 2019, Corning Incorporated announced that it had delivered its 2 millionth Corning® Varioptic® Lens for industrial applications. What drove this great milestone?Laune: This milestone was met thanks to the fact that Corning Varioptic’s solution solves several problems generated by classical motorized solutions used in industrial applications: limited number of actuation cycles, poor vibration and shock resistance, size (meaning bulky), and high-power consumption. Before Varioptic, there was no variable focus solution that worked well.In addition, the explosion of the CMOS sensor technology helped drive down the cost of imaging solutions for industrial devices, increasing the number of applications and shipping volumes.SEMI: What inspired Corning Varioptic Lenses?Laune: Varioptic was started in 2002 by Dr. Bruno Berge, a French physicist turned entrepreneur. Inspired by the work of Gabriel Lippmann, the 1908 Nobel Prize winner for the invention of color photography, Dr. Berge explored the shape-altering effects of an electric charge when applied to two liquids, a phenomenon referred to as electrowetting. His research ultimately led to the creation of liquid lenses.Fast forward to 2017, when Varioptic became a part of Corning through an acquisition that included Varioptic and Invenios technologies. We believe the synergies from this acquisition will lead to exciting new liquid lens application opportunities that align with Corning’s growth strategy and core capabilities. Corning is one of the world’s leading innovators in materials science. For more than 165 years, Corning has applied its unparalleled expertise in glass science, ceramic science, and optical physics to develop products that transform industries and enhance people’s lives.SEMI: What differentiates traditional camera systems from adjustable lens solutions?Laune: Traditional industrial cameras are usually fixed focus, meaning that the image is sharp only in a limited distance range. Unlike consumer camera applications, there were no good solutions for variable or auto focus cameras in the industrial space. This is due to the intrinsic limitations of motorized technologies.Therefore, customers were using, for example, several cameras to focus at several distances. This compromises the optical quality by closing the objective in order to increase the depth of field, therefore limiting resolution and leading to a need for more light.The cameras using Corning Varioptic’s technology offer more functionality with their ability to focus, whatever the distance, in a fast, reliable, and accurate fashion, and with lower power consumption than traditional mechanical solutions. The upshot is that the product that can withstand heat, vibration, mechanical shocks, and high numbers of focus cycles in tough industrial environments. SEMI: And how is electrowetting enabling industrial devices to capture images and process information quickly and clearly? Laune: In two words: fast and accurate.Electrowetting has unique features – with our two-liquid solution, we combine fast focus with high vibration and shock resistance, and the added benefit of low power consumption.What’s more, our programmable lens can be reconfigured on demand. The lens adapts rapidly and continuously from diverging to converging and can be modeled to support demanding variable focus applications. Our lenses can change their focus in milliseconds, similar to the human eye, and capture fast-moving objects at varying distances. The use of liquid, over mechanical solutions, allows us to create a small form factor, saving precious space and reducing power consumption.SEMI: What industrial applications are taking advantage of this technology? Can you name one example?Laune: 2D barcode readers and industrial vision are our main markets. There is also a strong adoption of our technology in medical applications.SEMI: What does the rise of machine vision mean for manufacturers? Give us one prediction about the opportunities offered by advanced imaging applications.Laune: A great example is the use of 2D barcode readers and liquid lenses to track your ecommerce order, point to point. Another example is full product traceability by implementing a 2D barcode on every component of a given product globally to improve product quality. The varying and adjustable focus abilities of our liquid lens technology make it possible for barcode scanners to track products of different heights, allowing manufactures to improve their processes and logistics.Beyond these examples, tracking and analyzing are booming, thanks to the combination of low-cost CMOS sensor technology, increasing processing power, innovative algorithms (deep learning, AI, neuromorphic processors, etc.), and better image quality due to the progress of lens technology, Varioptic being one.We see an opportunity to improve people’s lives, such as enabling better analysis of medical images and improving the use of cameras in biomedical technologies.SEMI: Quality inspection and automation, adoption of Industrial 4.0 technologies, government initiatives. If you were to choose one, what main factor will drive growth in the machine vision market?Laune: It is difficult to pick just one. I believe that full traceability (monitoring individual parts throughout the production process) has interesting implications as compliance and regulatory efforts ramp up and stronger security of goods becomes more important, particularly as consumers become engaged in food safety and tracing products throughout the supply chain.SEMI: What are your expectations for the SEMI MEMS Imaging Sensors Summit and why would you invite your peers to attend? Laune: I strongly believe in the power of human interactions in technology and science! Ideas come from discussions and physical interactions. The SEMI MEMS Imaging Sensors Summit is a great place to network, meet people, and think about the future! Frederic Laune is the business manager leading the Corning® Varioptic® Lenses business. Laune joined Varioptic as an R D engineer in 2003 after spending the first eight years of his career developing novel active components for the optical telecom industry. At the time, Varioptic was a newly created start-up aiming to develop liquid lens technology for industrial applications. After designing the first two Varioptic commercial products, the Arctic 320 and Artic 416, Laune stepped up as head of Varioptic’s R D department to focus on product and performance improvements. In 2010, he was appointed sales and marketing lead for the company. Varioptic was acquired by Corning Incorporated in early 2017. Laune received a master’s degree in physics and optics from University Pierre and Marie Curie (Paris) in 1995.Serena Brischetto is a marketing and communications manager at SEMI Europe.

Automotive original equipment manufacturers (OEMs) and their direct suppliers of parts and systems share a vision: Next-generation vehicles will be more electric, autonomous and connected. At a market size of more than $1 trillion, automotive is steadily becoming a high-tech market as cars morph into advanced technology platforms with partially or fully autonomous features. Call them semiconductors on wheels. Big players such as Google and many carmakers are investing heavily in chip advances to help drive increases in silicon content in automobiles.At SEMICON Europa, Pierrick Boulay, Solid State Lighting and Lighting Systems analyst at Yole Développement, will provide a market update on autonomous automobile trends including the state of sensors, radars, cameras and LiDARs as the industry works to increase the level of autonomy and electrification.Autonomous vehicle design can only thrive with the development of an industry standard for chip and device traceability across the supply chain. The importance of chip traceability to the automotive industry is reflected in its central role in driving a chip traceability standard.According to Heidi Hoffman, senior director of technology communities marketing at SEMI, “chip traceability is one of the next big things for the technology industry. The benefits are enormous, and the upsides – including yield enhancements, counterfeiting safeguards, and support for new applications – are plentiful. But the implementation challenges of chip traceability are also big and will require considerable effort to overcome. The biggest hurdle of all? We need to transcend industry fears by demonstrating that we can secure IP when it is shared across the hardware supply chain.” The Importance of Standards, Data Collection and Collaboration Across the Supply ChainThe automotive industry has long embraced tracing the sources of defects. Now, as the automotive and semiconductor supply chains increasingly overlap, traceability has taken on greater importance in the semiconductor industry. SEMI committees, task forces and events such as the Smart Transportation Forum at SEMICON Europa are ideal platforms for collaborating to develop new standards and best practices for the automotive industry.Earlier this year, German luxury automobile maker Audi AG became the first automotive original equipment manufacturer (OEM) to join SEMI as member, strengthening alignment across automotive supply-chain segments. At SEMICON Europa, the SMART Transportation Forum and Pavilion, staged by the SEMI Global Automotive Advisory Council (GAAC) and bolstered by the Electronic System Design Alliance, a SEMI Strategic Association Partner, will gather key stakeholders across the automotive value chain, from design and semiconductor equipment to materials and carmakers, to explore innovation opportunities in automotive electronics. SEMI Global Automotive Advisory Council (GAAC) “If the industry wants to reach the goal of zero defects, a new collaborative approach is necessary,” observed Antoine Amade, senior regional director EMEA at Entegris. At SEMICON Europa, Amade will present new ways to collaborate in reducing chip defectivity and meet other challenges in the automotive industry.More than half of semiconductor failures on the automotive assembly line today (so-called 0km failures) are traced to semiconductor fab defectivity. “The increasing semiconductor content in automobiles – driven by growth in ADAS, electrification and autonomy – has put a growing focus on the quality and reliability of these devices and their implications for consumer safety and satisfaction,” said Oreste Donzella, senior vice president and CMO at KLA.The smart manufacturing (Industry 4.0) revolution is already spurring higher performance and great efficiencies throughout the supply chain and will also be crucial to driving innovation in automotive. Smart manufacturing makes possible significant improvements in factory key performance indicators (KPI) for cycle time, on-time delivery, overall equipment effectiveness, cost and product quality.“These KPI gains are key to meeting quality levels the automotive industry must reach to support the deployment of autonomous driving vehicles,” said John R. Behnke, general manager of Final Phase Systems at INFICON. In his talk at SEMICON Europa, Behnke will provide an overview of existing, in-progress, and future smart manufacturing solutions for the semiconductor industry and their impact on the automotive supply chain. The SMART Transportation Forum, 13 November, 2019 (9:30-15:30 at ICM Munich, room 14c) at SEMICON Europa is the premier platform for key stakeholders to connect, collaborate and innovate across the automotive value chain. Automotive and semiconductor industry experts will offer insights into trends in design, semiconductor equipment and materials, and automotive innovation and the roadmap to 2030. The SMART Transportation Forum will also showcase innovations in imaging, sensing, artificial intelligence (AI), smart manufacturing and L5 mobility.Other SEMICON Europa highlights: Advanced Packaging Conference: Packaging and Test Challenges Towards High Reliability (12-13 November 2019) 23rd Fab Management Forum: Game Changers for Semiconductor Operations(11-12 November 2019) Strategic Materials Conference: Strategic Materials Enabling Industry Roadmaps(12-13 November 2019) SEMICON Europa registration is open for visitors and exhibitors. For more details, please visit the SEMICON Europa website and connect with SEMI Europe on Twitter or LinkedIn @SEMIEurope (use #SEMICONEuropa).Learn more about the SEMI chip traceability standard – SEMI T23 - Specification for Single Device Traceability for the Supply Chain – and SEMI Technology Communities.Serena Brischetto is a marketing and communications manager at SEMI Europe.

Semiconductor fabs have been getting smarter and smarter over the past 30 years. It’s a natural evolution – the direct outcome of numerous continuous-improvement efforts. The really important difference on the road to smarter fabs, the one change that’s enabling the Industry 4.0 revolution, is the concept of a cyber-physical system or digital twin. If you don’t have a thorough, detailed, high-fidelity digital twin of your entire fab operation, then you cannot have “Smart Manufacturing.” That’s really the definition of a smart site. A digital twin is simply a requirement for all smart factories of the future. One caveat: No matter what you build today from a smart perspective, your digital twin’s fidelity will improve over the next 20 years. A factory’s digital twin has two facets: the operational aspect and the yield aspect. Each of these two facets places different requirements on a database including the types of data, the frequency of data generated, the retention of data, and even the AI/ML techniques used to analyze the data. A combination of these data requirements are needed to create a digital twin – the virtual representation of your entire factory operation, whether it’s on the wafer-fab front end or the assembly and test back end. What’s most important here is that facility-wide data sets and databases must be able to communicate with each other using refined summary statistics to create a practical digital twin. For example, a lot of information is collected on the yield side to feed the deep-learning models needed to manage processes. However, the factory scheduler, driven largely by the smart operational database, needs only summary statistics from the yield database to be able to act in the next moment or over the next 24 hours. Figure 1 illustrates the needs of and the interaction between a smart operational and a yield database. Figure 1: The Operational and Yield databases in a Smart Factory need to exchange summary statistics. Today, we find that although these databases generally speak to each other in smart factories, they’re still not sufficiently connected to permit the use and analysis of data needed to realize the full potential of a smart factory. That level of interconnectedness is still in the future. Some solution providers have created what is essentially a “smart learning warehouse” (“database” has become too limited a term here). This warehouse collects, analyzes and learns from the extensive amount of information that a fab generates. Game-changing, more holistic applications become possible when this information can be combined in new and informative ways. As it turns out, a data source is just a data source, but users in different factory areas need to extract different information from these common data sources. They need different applications and portals – in other words “views” – that are adapted and adjusted for each area’s needs. Aren’t we smart enough? Some people think that 300mm fabs are already smart. That’s true. They are. But, they could be a lot smarter. No 300mm fab in use today has attained the full, utopian vision of what a smart factory can deliver over the next 10 years. When you finally integrate all of the disparate databases in a fab – when you’re able to use all of those different data sources as one common data source – that’s when your Smart Factory will have the ability to self-optimize its future actions and react quickly to real-time events. The largest semiconductor manufacturers tend to develop these smart factory applications on their own. The remaining semiconductor fabs need to work together with other fabs and their solution providers to develop these smart factory applications. Why now? Why is everyone talking about “Smart” now? It’s because the semiconductor industry has helped to create all of the enabling technology: the compute power, the networking and networking standards, and even the industry’s maturation into a multi-tiered organization of solution providers. We’ve reached the point where we can collect data from a widespread sensor network along with tool-health data and we can then warehouse this data so that it can be applied to more intelligent decision-making. While there may be one or two sensors on a tool today, in the future there will be many such sensors connected over an IoT network or networks that provide mountains of data to the warehouse. All of this data will feed into the digital-twin version of the fab. One of the biggest changes on the horizon made possible by all of this accessible data is advanced scheduling. Despite all of the automation advancements made over the past 25 years, including robotic handling, it’s still hard to decide “where, what, and when?” for every single lot in the factory. Today, no factory in the world is more complex than a semiconductor fab. Optimizing a semiconductor manufacturing process is the most complex manufacturing-optimization task in the world. Do it for ROI ROI is the chief reason for having a digital twin. Once you can make a truly smart, holistic schedule of the fabs operations — not a dispatch or rule-based dispatch list — then you can create an operationally smart factory. Rule-based dispatching systems primarily focus on tools and tool-centric views. Although they incorporate knowledge from current WIP and tool conditions to make decisions better than simple dispatch systems, smart factories are not just about tools and the current WIP at them. Smart factories use the status of every tool and lot in the factory to make fab-centric optimizations instead of tool-specific optimizations. Once you have a digital twin, you’re optimizing for global functions such as line linearity and on-time delivery. These functions are not just about the moment. The transition to a smart factory thus represents a huge philosophical change. When you know exactly what’s going to happen in a factory over the next 12 hours for every single lot, every single wafer carrier, and every single entrance port of every tool in the factory, then you suddenly have control over the factory’s idle time. You know when you can optimally perform PM (preventive maintenance). You know how to best redirect material or labor resources to maximize output. You can create a smart schedule for every maintenance person in the factory that comprehends each person’s skill set and tool downtime so that there’s no negative impact on the factory’s productivity. You can only do all of this when you know the future. Figure 2 illustrates the opportunity. Imagine that a factory contains 1,500 tools. Use of these tools is scheduled for the next twelve hours. The information depicted in Figure 2 encompasses process changes from one chemistry to another, implant changes, reticle changes, and the status of every single consumable for all 1,500 tools. The white spaces that appear between processes in Figure 2 represent opportunities to intelligently schedule events such as maintenance to maximize factory productivity. Figure 2: Smart scheduling permits factory-wide optimization to maximize productivity. Once you have a schedule, you need to translate that schedule into actions or movement. It’s not easy to do this and most material-control systems today make overly simplistic decisions based on modeled assumptions and typical cases rather than the actual time each lot needs to be at a precise location, which can only come from a schedule. Once the data from all of the tools is connected, a smart scheduling system can use the digital twin to make far better process decisions. The larger the factory (or more complex the factory), the more important it is to make smarter decisions. Note: SEMI has a Smart Manufacturing Technology Community. For more information or to get involved, click here. If you would like to discuss Smart Manufacturing more with John directly, he can be contacted at [email protected]. John Behnke is general manager of the Final Phase Systems product line at INFICON.

Part 2 of this two-part piece examines the potential benefits to be realized by pairing human Subject Matter Experts with smart silicon assistants, and what these new arrangements mean for semiconductor device manufacturing. Part 1 explores best-practice perspectives on collecting and utilizing smart data in industries outside semiconductor manufacturing, one of the important takeaways from the Smart Manufacturing panel discussion at SEMI ASMC 2018. So what does this observation (i.e. the field of medicine, in what seems at first glance a big data environment, is really just clusters and clusters of loose small data connected by the collective neural network of highly trained doctors and their colleagues) mean for semiconductor manufacturing? We think it means we need to apply the same level of intense focus that we already devote to instrumented data collection and analytics in the fab to something more: we need to better capture the vast expertise of our engineering and operational talent in semiconductor manufacturing. We think we need to record what the subject matter experts (SMEs) in the fab see, hear, and think as they investigate yield excursions or machine-down problems. We need to effectively combine product, process, equipment and component subject matter expertise / subject matter experts (SME) with big data analytics to more effectively solve manufacturing problems, be they killer or be they chronic. And we must provide structured methods for incorporating inputs from and active participation of SMEs throughout the data analysis lifecycle, from collection and aggregation, through filtering, feature extraction, analysis and optimization. Some of the challenge will be in just how do we make it easy to gather information from SMEs in real time, while standing in front of equipment in the fab. Internet of Things (Iot) devices are emerging to capture and label images and sounds to enable machine learning algorithms to recognize and help diagnose manufacturing problems based on sight and sound, complementing the instrumented data. But we also need to record the thought processes our human SMEs go through in those investigations – perhaps by the SMEs talking to a smart AI-based conversational assistant who helps make “rounds.” Doing contextual analysis on this added data, combined with the instrumented data, will create the equation Human + Machine = AI (Awesome Insight). Sounds reasonable, right? We think artificial intelligence becomes too artificial if you leave the human out of the equation. AI should be augmented intelligence, where we take the expertise and creativity of the human, and combine it with the rapid computational capabilities of the computer, in order to put problem identification and solutions on steroids. But with the already huge advancements to date in data analytics, cloud, and the emergence of AI, why do improvements in quality, machine utilization, and the implementation of predictive analytics in semiconductor manufacturing seem to be creeping along incrementally, and not appearing as dramatic, step-function improvements? Call it Smart Manufacturing, call it Connected Enterprise, call it Advanced Manufacturing, or Analytics, or Cloud, or the Digital Twin … there are no shortages of terms, philosophies, and technologies available, but why aren’t we seeing their rapid adoption? It could be it’s the downside that comes with needing people. “Good business leaders create a vision, articulate the vision, passionately own the vision, and relentlessly drive it to completion.” Jack Welch. We see from other industries that smart manufacturing conversations originating with the executives of a company thinking to implement smart manufacturing programs lead to vision; however, we also see from other industries, and from our own, that realizing this vision has often been a challenge. Why is that? One reason may be that the people who are personally vested in solutions they implemented in the past, as well as those who follow a pattern of ‘how we’ve always done things’, create, inadvertently or not, persistent internal barriers hindering innovative action. Another may be that engagements with the working engineers and managers charged to be smart manufacturing implementers leads to the pursuit of low-hanging fruit, and cautious investments, that often utilize solutions that ultimately cannot scale and integrate. Not to mention the disadvantage of dealing with the legacy equipment, the legacy networks, the traditional thinking, and the lack of consistency in metrics adding to the confusion. Addressing all these barriers requires an alignment in strategy and execution, along with a plan to support the overall vision, often across the entire enterprise, which is no small matter. And then there are the standards. Having and adhering to standards in control solutions, networks, and data becomes critical in achieving real benefits from smart manufacturing. And data security. One of the other big impediments in the smart manufacturing transformation is data and IP security, another key concern (maybe the most significant) preventing us from moving forward more quickly (e.g. to cloud-based solutions) in our industry. More about that in a follow-up. Achieving synergy across all of manufacturing, from connecting equipment horizontally, through the production system (machines processes), and vertically, through enterprise systems and across production facilities, can only occur if we build standards, security, infrastructure, and human engagement throughout our ecosystem and supply chain. In simple form, the steps to do so include connecting assets, collecting and contextualizing data, and then driving business transformation with actionable insights gained from the data. With impact on every function, and every person, in the enterprise, from equipment operators in the fab through the C-Suite in HQ. Maintenance, Engineering, R D, Operations, Scheduling, IT, Procurement, Finance, HR all contribute, collaborate and benefit. Regardless of the technology, from device level analytics to predictive maintenance and optimization, the people that reside in these disparate groups need to come together with the smart machines to create a common strategy to achieve transformational results. Aligning an enterprise’s goals with its human capital is paramount to success. Therefore, we must challenge our team members and ourselves to work outside our comfort zones, and we need to be forever aware of the need for us to grow with the technology. Smart manufacturing is not necessarily about having fewer people in the fab, but it does suggest having people in the fab, perhaps with different, or upgraded, skill sets, who are even more efficient in their roles as a result of the boost they are getting from Industry 4.0. Fortunately, we now have techniques that let us combine the best, brightest, and latest and greatest analytics with our invaluable SMEs throughout the data analysis lifecycle. We’ll not only be able to deliver higher quality semiconductor manufacturing solutions all in all, but we’ll also be providing methods to more easily distribute, scale, maintain, and continually refine those hard-earned solutions. We expect that subject matter experts will continue to put the “smart” in machine-based smart manufacturing today, and for the foreseeable future. SME contributions are not an option, but, rather, an imperative for ensuring a semiconductor manufacturer’s sustained prosperity, much less its survival. Nancy Greco (IBM Watson), Dave Mayewski (Rockwell Automation), James Moyne (University of Michigan / Applied Materials), and Paul Werbaneth (Intevac, Inc.), along with Julie Jacob (Ernst Young), and Carson Henry (Micron Technology), were members of the SEMI ASMC 2018 panel discussing Industry 4.0 and the Future of Commercial Semiconductor Device Manufacturing. All opinions here are purely our own. Please contact Paul Werbaneth via email at [email protected]. The SEMICON West (July 9-11, 2018, in San Francisco) Smart Manufacturing Pavilion features working production equipment on the floor and three full days of speakers providing insights on building the infrastructure needed to enable AI. Equipment from Bosch Rexroth, Cimetrix, Rudolph Technologies, INFICON, Final Phase Systems, OMRON, DISCO and Edwards Vacuum will showcase cutting-edge smart manufacturing technologies. For information on the SEMI Smart Manufacturing initiative and how to get involved, please click here.

Part 1 of this two-part piece explores best-practice perspectives on collecting and utilizing smart data in industries outside semiconductor manufacturing, one of the important takeaways from the Smart Manufacturing panel discussion at SEMI ASMC 2018. Part 2 examines the potential benefits to be realized by pairing human Subject Matter Experts with smart silicon assistants, and what these new arrangements mean for semiconductor device manufacturing. The spacecraft Discovery and its HAL 9000 computer system had a digital twin. Did you know? Stanley Kubrick’s seminal film “2001: A Space Odyssey” had its theatrical release 50 years ago this April. “2001” isn’t just a great science fiction film. Rather, it’s a great work of cinema overall, across any category. (The American Film Institute lists “2001” as #15 in the AFI Top 100; a bit below “Vertigo,” a bit above “It’s A Wonderful Life.”) It’s a film so distinguished and so prescient that its lessons can inform our thinking about smart manufacturing, Industry 4.0, and artificial intelligence (AI) today. Not to give too much away, but the earth-bound digital twin of Discovery / HAL identifies a diagnostic error the onboard, Jupiter-bound HAL 9000 has made, things go awry from there, and one of the mission pilots, astronaut Dave Bowman, is forced to intervene. At the recent SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018, on 02 May 2018 in Saratoga Springs, NY, five diverse panelists representing capital equipment, IDMs, academia, the semiconductor supply chain, and smart manufacturing best practices outside the semiconductor industry engaged in a lively discussion with the ASMC attendees. They explored where “smart” is in our industry today, where it’s headed, and what that’s going to mean for us -- the professionals who have brought semiconductor manufacturing to the current state of smart, and are looking to implement an ever-smarter tomorrow. Not to give too much away, but the panelists and audience agreed that there’s nothing artificial about pairing human intelligence with machine-based smart manufacturing. Implementing an ever-smarter tomorrow in semiconductor manufacturing requires smart people just as much as it requires smart machines. Moving towards “smart” means understanding how to derive useful information and actionable intelligence from the ever-increasing pool of big data created during semiconductor manufacturing. Modern manufacturing sites are extensively instrumented today, and create massive amounts of data to consume, decipher, base decisions upon, or discard. As we dig into this problem we realize that equipment and processes in our industry are both obviously complex, but, also, subtly complex. Semiconductor manufacturing tools easily contain 100s to 1000s of components working together to produce nanometer scale, angstrom scale, or even atomic scale features using complex chemical, physical, and plasma processes. There is a plethora of potential failure points and modes, and despite our best efforts to collect more data, many processes continue to be only poorly observable. On top of that, semiconductor fabrication processes are always drifting, and the operational context is continually changing as we change product mix, process maintenance swap-out kit components, and operating conditions and recipes. Sounds like … hospitals, and healthcare? When you see your doctor, she will collect and look at your instrumented data – blood work, blood pressure, weight, and other quantifiable factors. But, typically, your doctor won’t draw a conclusion based on that analysis alone. Rather, your doctor will sit with you, ask probing questions, and record what she asked, your responses, and what she saw, what she heard, and what she thought. Then she’ll build a hypothesis, combining the “anecdotal” data with the instrumented data, and derive from that data set both a likely diagnosis and an effective course of action. In this case, beyond the instrumented data, two humans, and their natural language input, are part of the equation: the patient, with his observations and thoughts, as well as the doctor, with hers. And it’s been a formula for success. Healthcare has made huge, step-function improvements across a spectrum of deadly diseases, as well as with less-deadly chronic afflictions, by harvesting this complex input, committing the proven disease presentation – disease diagnosis – and disease treatment models to medicine’s collective memory, and then teaching the next generation of healthcare providers both the general methods and the standard protocols essential to maintaining good health and successful outcomes. Maybe, in medicine, what seems a big data environment is really just clusters and clusters of loose small data connected by the collective neural network of highly trained doctors and their colleagues. Nancy Greco (IBM Watson), Dave Mayewski (Rockwell Automation), James Moyne (University of Michigan / Applied Materials), and Paul Werbaneth (Intevac, Inc.), along with Julie Jacob (Ernst Young), and Carson Henry (Micron Technology), were members of the SEMI ASMC 2018 panel discussing Industry 4.0 and the Future of Commercial Semiconductor Device Manufacturing. All opinions here are purely our own. Please contact Paul Werbaneth via email at [email protected]. The SEMICON West (July 9-11, 2018, in San Francisco) Smart Manufacturing Pavilion features working production equipment on the floor and three full days of speakers providing insights on building the infrastructure needed to enable AI. Equipment from Bosch Rexroth, Cimetrix, Rudolph Technologies, INFICON, Final Phase Systems, OMRON, DISCO and Edwards Vacuum will showcase cutting-edge smart manufacturing technologies. For information on the SEMI Smart Manufacturing initiative and how to get involved, please click here.