Big Data

SEMICON Korea 2023 showcased the latest developments in high-priority areas including sustainability, smart manufacturing, advanced chip technologies, and workforce development. Keynotes by semiconductor leaders AMD, imec and Lam Research on critical chip industry trends.

For business leaders implementing AI projects, these hurdles can lead to frustration with poor results, excessive costs and delays, and even actual harm and lawsuits in extreme cases. Addressing these challenges is critical for realizing the full potential of AI.

Guido D’hert, High Tech and Semiconductor Industry Europe Lead at Accenture, spoke about challenges semiconductor companies face in meeting sustainability goals and how to apply analytics, new green technologies and circular design, sharing during his presentation at ISS Europe in Brussels, Belgium.

More than 4,000 participants convened in person at the four-day SEMICON Europa Expo in Munich, November 16-19. Attendees gathered at the Executive Forum for insights into the latest trends in the semiconductor industry and heartily welcomed the long-awaited return to in-person networking.

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.

Ride the Wave of Smarter Manufacturing The year 2020 sparked a tremendous acceleration in the digital transformation worldwide, driving a sharp rise in demand for semiconductors and escalating pressure on chip factories to reduce manual functions on the shop floor. The mindset of the semiconductor industry saw a remarkable shift as it recognized with heightened urgency the need to deploy data-driven visualization, analysis, scheduling and dispatching solutions to increase automation to improve production speed and efficiency. Amidst the new excitement around Industry 4.0, chip manufacturers are rapidly deploying new technologies including IIoT, big data, machine learning and Autonomous Intelligent Vehicles (AIVs). Yet for many chip manufacturers, the path to building a smart factory is far from clear because they lack an overall digital transformation strategy. Smart manufacturing is a broad concept covering an array of technologies and solutions, making a holistic, mid- to long-term digitalization strategy rooted in the overall business strategy crucial. There are no shortcuts that can move a manufacturer instantly to Industry 4.0. Instead, this transformation is a step-by-step undertaking with a natural evolution. Some Factory Tasks Must Remain Manual – For Now The semiconductor industry has reached a point where manual processes are no longer efficient enough to support mass chip customization and remote operations. The many technological and standardization advances behind automation can help streamline some of a factory’s most labor-intensive tasks including the loading or unloading of machines or lot tracking and data collection while reducing operational costs. Still, some tasks remain very difficult to automate. For example, handling errors and exceptions presents the greatest challenge since some errors are hard to anticipate. What’s more, the cost of automating error handling can be prohibitive. Eliminating Gaps in Connectivity Often, critical data sources aren’t available due to lack of equipment integration, incomplete product quality monitoring or gaps in material tracking. Closing these gaps in connectivity enables the collection of data and provides rich, reliable information for analysis and reporting that can drive continuous operational improvements, optimizations and efficiencies throughout a factory. But keep in mind that data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even when data is available, it might be still difficult to make decisions or implement improvements if it is in siloed systems that require manual processes to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. Manual integration is not only ineffective but costly, draining time, human resources and money from the factory. The right contextual information for the data is vital to unleash its potential and make improvements possible. Dispersed solutions cannot control processes because they span functional areas and people, physical and business entities. Backbone software for shop-floor operations that controls all other applications is central to smart manufacturing. Data-Driven Manufacturing The semiconductor industry is expert in data collection and leads many other industries in this area. The problem is often that chip companies use only a fraction of the information they collect for the analysis and insights needed to improve operational efficiency. By comprehensively integrating all distributed data into a single version of truth – in one location where it is always available – companies can make data analysis and problem solving almost frictionless. Keep in mind that data platforms and edge solutions, within the context of manufacturing, will not be adopted as part of a greenfield initiative. Building a solid automation architecture is only feasible and beneficial by deploying new technologies such as machine learning and artificial intelligence (AI). Analysis of historical data provides important context and reveals deviations such as unexpected process time, uncommon material accumulations or issues with material transport. By integrating swift control actions for new data point collected, manufacturing operations can shift from reactive problem-solving to proactive analysis and operational improvements. The tremendous increase in interest and investment in AI for manufacturing automation only became possible with the availability of low-cost sensors that generate huge volumes of data and solutions for storing and processing that at low cost. AI and other leading-edge technologies transform the tedious but critical process of extracting insights from data, making it instantaneous, streamlined and achievable for every manufacturer. The maturity of smart manufacturing hinges on the extent to which a factory is data-driven. This requires foundational investments to improve traceability, connectivity and real-time operations – and finally making sure that data helps us what to do and when to do it. Ricco WALTER is managing director of SYSTEMA Automation in Singapore.

The costs of production are typically based on labor and materials and define manufacturing expenses. But is this approach accurate enough? What about the cost of poor quality and lack of efficiency in production? How is the pandemic impacting semiconductor manufacturing and what can we expect from the future?SEMI recently spoke with Dr. Eyal Kaufman, founder and CEO of QualityLine, a Kiryat Gat, Israel-based provider of smart manufacturing analytics solution, about manufacturing controls and how to select the best data source to improve product quality and yield. Kaufmann provided a snapshot of current best practices used by the company to improve manufacturing efficiencies and product quality while reducing costs. He also discussed the COVID-19 pandemic’s impact on semiconductor smart manufacturing and how artificial intelligence (AI) can help keep factory workers safe.For additional insights on smart manufacturing, join the virtual SEMI Global Smart Manufacturing Conference, October 20 - 22, 2020. Registration is open.SEMI: Real manufacturing costs are calculated based on different aspects such as failures in production, repairs, products returned, scrap of components or late deliveries. Lack of quality and efficiency in manufacturing can undermine a business. How are you helping businesses overcome these challenges?Kaufman: To increase profit margins, it is essential to identify inefficiencies and what improvements to prioritize. Once manufacturing quality and efficiency deficiencies have been measured, the next step is to continuously collect manufacturing data in order to run the final cost analysis and use the analytics to improve the manufacturing process.Smart manufacturing makes it possible to detect anomalies in automated factories, improve production performance and increase profitability. Today, automated data are collected from every machine and piece of test equipment in the factory. Still, manufacturing data collection in many industries remains manual and expensive because of the time and human resources involved. A real-time analytics system can automatically collect all data sources and select the relevant data for analysis, which today is the most accurate and effective way of measuring and resolving quality and efficiency deficiencies.Data-driven decisions made by smart manufacturing reduce costs and improve manufacturing strategies, enabling factory operators to increase product quality, drive higher production capacity and enhance product design for manufacturability. Analytics solutions monitor shop floor operations accessing vendors and subcontractors’ products criterion to run root cause analysis. All those data will reduce the return rate of faulty products and accelerate return on investment. This is why we definitely need smart manufacturing technologies!SEMI: Data accumulated during the manufacturing process includes vital information about failures, anomalies and machine usability. What data are necessary to create the best analytics solution?Kaufman: Many companies today run data mapping and automatic creation of data capture. They often wonder if they need to use testing data, sensors data or product design data, or whether they should collect feedback from their customers and vendors. The best way to create an effective manufacturing analytics system is to use data sources such as: Feedback from customers (returned units, customers complaints, etc..) Testing data from automated test equipment and manual test activities Feedback from technicians repairing faulty units Analysis of testing processes done by vendors Sensors data Data from our ERP/MES systems Artificial intelligence enables any type and size of data structure, even accumulated data, to be automatically integrated and interpreted. AI-based analytics can also establish correlations between each manufacturing stage to help factory operators quickly conduct deep diagnostic and root cause analysis for problem solving and prevention – all while leaving intact a factory’s existing process, machinery and data output. Machine learning evaluates how a factory runs its database and puts all the information generated into an analytics solution that provides the know-how to continuously improve factory efficiency.SEMI: How do you select the best data source to improve manufacturing quality and yield? Kaufman: The accuracy and integrity of data accumulated in our manufacturing process is key to controlling and improving yield and quality while reducing manufacturing costs. Smart manufacturing is a technology-driven approach that uses digital and remote connected machinery to monitor the production process. The goal is to identify anomalies in manufacturing processes and leverage analytics to improve process yield and product quality.To select the relevant data, we collect each type and source of data that can improve the efficiency of a real manufacturing cell: Test data from Automated Testing Equipment Test data from Manual Testing Processes Analyses of repairing processes (failed units during the manufacturing process and units that were returned from customers) Once the data structure is collected, the next step is to turn it into actionable information in the manufacturing process. QualityLine smart manufacturing solutions provide a complete one-stop solution to interpret any manufacturing data structure. Our advanced manufacturing analytics solution detects quality and yield anomalies to reveal production line inefficiencies and opportunities to improve manufacturing quality and efficiency.SEMI: How would you describe your approach?Kaufman: Industry 4.0 in manufacturing claims to be the fourth generation of the industrial revolution. Advanced technologies like manufacturing intelligence and machine learning can efficiently achieve zero defects on manufacturing lines. Digital factories leverage technologies and methodologies including: Big data Self-optimization Self-configuration Self-diagnosis Cognitive and machine learning Smart manufacturing technologies enhance the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain high quality performance. The goal is to achieve a significant increase in efficiency and yield while reducing waste and inefficiency.Until now, there has been no viable way to integrate all saved manufacturing data into a unified database. QualityLine advanced manufacturing analytics make it possible for any factory to become digital without installing new hardware, which can be expensive and require not only the extensive integration of existing data but investments in training. Our user-friendly solution integrates manufacturing data for industries with zero automation by first collecting and analyzing data from any type of manual test procedure and then integrated it into manufacturing analytics to improve efficiency.SEMI: Why are Pass/Fail criteria insufficient for controlling manufacturing yield and quality?Kaufman: Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before products can ship to customers. At QualityLine, we establish a test process for each stage of the production flow, from the incoming raw material to the final stage prior to the delivery of finished goods to the client. To prevent unexpected downtime incidents, waste and defective products, we collect and interpret every type of relevant data and turn it into meaningful information, setting up the following capabilities: Collection and interpretation of test and process data of each single unit and from each process and plant Automatic detection of quality and yield problems Accurate and quick root cause analysis process Automatic alerts to abnormal issues Prediction process potential and level of failures Measurement of key performance indicators Many manufacturers base their test criteria of each parameter on one key indicator – Pass or Fail. If the test result shows a Pass, then the unit is ready to move on to the next manufacturing stage. If the test result shows Fail, then the unit is sent to a technician for further analysis.A simple Pass or Fail criteria for product quality is far from sufficient since it provides little or no information about edge cases, where one or more of the technical parameters of the unit under test is only within its allowed tolerance. Edge cases may lead to unit failure during operation such as in extreme environments (cold, heat, humidity, electrical overload, impact, etc.). In fact, when running a mass manufacturing line, it is impossible to continuously digest all the detailed information collected from testing stations. Data is analyzed in detail only when a critical quality problem emerges and further analysis is required to understand the root cause.Information overload and the disregard of important parameters makes it hard to control the process and improve quality and yield. New technologies make fast and scalable data integration possible so data can be collected in real time to detect quality issues early, identify complex process disruptions to avoid delivery delays and ensure the best possible product for customers. Only by accurately analyzing data as actionable information can factory operators control the manufacturing quality process.SEMI: How has COVID-19 impacted the smart manufacturing market? How has your technology helped factories remain online?Kaufman: Smart manufacturing is playing a significant role by helping manufacturers overcome COVID-19 challenges such as workforce reductions, social distancing, drops in sales for some specific products and extreme pressure to cut operational costs.Manufacturing leaders turned to us for a solution to the challenges of maintaining efficient factory operations with a limited workforce and reduced number of operating hours. Filling factory orders with fewer people on the floor is a struggle. Digital factory technologies enable remote monitoring of operations to increase efficiency and capacity. We are helping our clients improve efficiency while reducing costs. Our remote monitoring technology can provide the operational visibility to floor managers and engineering teams who cannot go physically to the factories due to safety restrictions. With our advanced manufacturing analytics, they have full end-to-end visibility and can remotely diagnose and solve production line issues. During this critical time, we are proud to be improving remote monitoring solutions to help the industry withstand the pandemic. Some of our clients would have closed their factories otherwise. We’ve been working to integrate manufacturing data in factories that were previously unautomated to drive high automation levels. Integrating processes with existing factory data, regardless of customer’s protocols or automation level, is our great technology advantage.SEMI: How will manufacturing and its supply chains look after COVID-19?Kaufman: Smart manufacturing is currently a necessity. We collect and analyze data not only to improve quality but to reduce client returns of faulty products by 50% and reduce waste by 22%, both critical points. Manufacturing challenges will continue to accelerate advancements in technology and improve efficiency, safety and productivity as more factory operators incorporate real-time data analytics and artificial intelligence (AI). SEMI: Will suppliers continue to explore new avenues for smart manufacturing technologies and what are their growth opportunities?Kaufman: Yes, definitely. The sector has already changed, with COVID-19 bringing both opportunities and challenges. Industry leaders are facing new pressure, with sudden materials shortages, drops in demand and worker unavailability. The growth opportunities for manufacturing are likely to be digital, as already evident in the immediate response to the crisis. Industry 4.0 solutions will be crucial to increase end-to-end supply-chain transparency, automation and data integration. QualityLine manufacturing analytics have improved key manufacturing performance metrics. For example, based on customer feedback, we’ve increased production yield by 30%, saving some of our customers millions of dollars. Improvements like this can help suppliers withstand pandemics.Dr. Eyal Kaufman, Founder and CEO at QualityLine, has senior management experience and over 25 years of expertise in business development, marketing, finance, operations, engineering and quality management at leading industrial companies. Prior to QualityLine, he served as VP of Mobileye, Cardo Systems, and Medisim Ltd., as well as CEO of OnTheGo Systems. Eyal holds a Ph.D. from California Intercontinental University, an MBA from City University of New York and a BSc. from the Technion in Israel.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness and interest about smart manufacturing with focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality, productivity and cost improvements through smart manufacturing. Activities are focused on building out core capabilities to enable smart manufacturing across the microelectronics supply chain.MADEin4 is a consortium of 47 partners from 10 countries connecting the full range of supply chain: from semiconductor equipment manufacturers and system-integrating metrology companies to RTOS and key applications such as the automotive industry. The MADEin4 Project develops next generation metrology tools, machine learning methods and applications in support of Industry 4.0 high volume manufacturing in the semiconductor manufacturing industry.Serena Brischetto is a senior manager of marketing and communications at SEMI Europe.

MEMS and image sensors are shining stars in the chip industry as technology companies worldwide accelerate innovation in the fight against COVID-19. The tiny devices are behind advances in areas of electronics ranging from thermal imaging and faster point-of-care testing to microfluidics-based polymerase chain reaction (PCR) tools and techniques to detect SARS-CoV-2.SEMI recently spoke with Yole Développement analysts Dimitrios Damianos and Chenmeijing Liang about MEMS and imaging sensors market trends and how microelectronics-enhanced technologies are supporting the worldwide push to contain the spread of COVID-19.For additional insights on the technologies, join the SEMI MEMS Imaging Sensors Summit, held for the first time at SEMICON Europa, 12-13 November 2020 in Munich, Germany. Registration is open.SEMI: Despite the global pandemic, the MEMS and sensors market is still growing and is one of the healthiest industries, not only in Europe, but globally. What is driving this growth?Damianos: MEMS have been continuously evolving from the first sensors that were measuring pressure and acceleration to rotation sensing and visible light management followed by light sensing beyond visible and the expansion to ultrasound and multi-spectral. Now we are heading towards an era where we want to sense every aspect of our environment, with more processing and eventually analytics bringing more quality to the data.COVID-19 has impacted various global markets in very different ways. While automotive, mobility and civil aviation have suffered, the impact on telecommunications and medical has been positive. The effects on the consumer, mobile and industrial markets have been moderate. Moreover, COVID-19 is changing the perception of the current global supply chain in manufacturing, potentially leading to more localized value chains and further regionalization in order to minimize similar risks posed by the pandemic and the first lockdown.SEMI: Who are the main MEMS players based on your research? Damianos: For MEMS players, the picture in 2019 was not the same as 10 years ago, when Texas Instruments (TI) and Hewlett-Packard (HP) were leading the scene, with Bosch and ST Microelectronics following, all at comparable revenue levels. Now, Broadcom and Bosch lead with almost $1.4 billion in revenue each, and the rest of the MEMS key stakeholders compete in the $400 million to $600 million league. Microphone players profited from the voice interface adoption trend, while players active in MEMS for mobility and smartphones suffered slightly due to weak end-system demand.SEMI: What scenarios can we expect for each market with regard to the impact of COVID-19 on MEMS for 2020? Damianos: For 2020, at Yole Développement we expect the consumer market to contract slightly by 2.6%, with the automotive market to dip by 27.5%, and defense and aerospace by 20.5%. For the defense market, no major effect is expected, as all major programs still run for the year. The market may experience some slight delays in deliveries due to supply chain and logistics problems. However, sensors integrated in commercial/civil aerospace applications will suffer due to the general paralysis of the air travel industry. On the positive side, telecommunications could increase by 4.7%, medical applications by 10.6%, and industrial by 11.5%.Due to the global pandemic, some types of MEMS have spiked in demand this year. For example, demand for thermopiles and microbolometers used in temperature guns and thermal cameras has increased because of the need for contactless monitoring of people’s temperatures. Moreover, microfluidics for DNA sequencing and real-time polymerase chain reaction (PCR) diagnostic tests for detecting COVID-19 are gaining market relevance, with the latter serving as a premier method of detecting a bacteria or virus on the molecular level with high degrees of accuracy. Furthermore, pressure and flowmeters in ventilators will grow because of huge demand by hospital intensive care units (ICUs).SEMI: What growth trends do you predict for the long haul?Damianos: In the longer term, we expect global MEMS volumes to almost double, from 24.4 billion units in 2019 to 50.8 billion units in 2025, with a 13% CAGR during the same period. The global MEMS market could reach $17.7 billion in revenue by 2025.We see a trend to more wearable devices integrating a lot of sensors but also a move to a more consumer-oriented healthcare. Moreover, everything related to voice interfaces and voice/virtual-personal assistants (VPAs) will continue to see strong growth, increasing demand for MEMS mics with better quality and high-fidelity voice capture. MEMS devices are shifting to higher accuracy, ultra-low power, embedded intelligence and possibly some bio-compatibility for medical applications.MEMS players will try to escape the commoditization cycle and deliver more value by increasing the value of the data, either grouping many sensors to create sensor hubs or by adding processing, algorithms and software. Industry players are employing strategies such as adding extra processing close to the sensor (e.g. Knowles) or ameliorating the use cases of their applications of their clients (e.g. Bosch or ST). AI on the edge seems very alluring for extra value acquisition, with many startups already working on it. Some examples include always-on-sensing (Aspinity in collaboration with Infineon, Syntiant), echolocation (IMERAI) and predictive maintenance using inertial sensors (Cartesiam). This will be the next pit stop for MEMS technology for sure. SEMI: The CMOS Image Sensor (CIS) is a cornerstone technology in the development of devices powered by machine sensing and artificial intelligence (AI) for applications such as advanced driver assistance system (ADAS). CIS powers many of the ongoing revolutions in new technical products and use cases. What is the status of the image sensors industry? Liang: Last year was exceptional with a combination of high demand and high prices due to capacity limitations. Q4 2019 went way above the forecast, and, in the end, the CIS industry reached $19.3 billion for the full year. This year, we think it will return to normal, and, despite the pandemic impact, we expect significant growth in the range of 7% to 12%. Last year’s 25% year-over-year (YOY) growth was the highest we’ve seen over the past decade. Mobile still dominates the marketplace for CIS with 69% market share. Two markets, computing (8%) and consumer (5%), are adjacent to the mobile market but progressively losing ground due to the smartphone disruption.Security, at 6% market share, will probably be the second largest CIS market in the future. Although this is an area of excellence for the emerging Chinese players, unfortunately, they could be hit by the current trade war. The automotive market did very well from 2018 to 2019 because of the numerous applications recently developed for ADAS, viewing, and in-cabin applications. Lastly, the industrial camera applications benefited from large investments in automation, especially in the semiconductor and automotive industries, but here again many uncertainties remain as these markets will reshuffle in the post COVID-19 world. SEMI: Which CIS markets are most susceptible to seasonality and the impact of COVID-19?Liang: According to our quarterly CIS monitor, automotive and security were both negatively impacted by the pandemic beyond what we expected in terms of seasonality. For computing, the situation improved just prior the lockdown. Q1 got a positive impact with high sales results for laptops and tablets, but no significant impact was seen for security equipment. For automotive, the demand for cameras was very high in Q1, which is seasonally normal, despite the decrease of car shipments that followed later. The automotive CIS market in 2020 should remain relatively flat compared to 2019 due to the higher attachment rates of cameras despite the lower number of cars produced. Consumer and industrial segments dropped in Q1, which is typical early in the year.The next five years might be a bit slow, and although we forecast growth for the next year, in the future the market share will be lower in mobile. In fact, mobile CIS growth will fall below the CIS growth average, but we will see an increase of market share for the security, automotive and industrial segments. The CIS market could reach $28 billion in 2025.At first, COVID-19 had a limited impact on the production side, as factories in China are usually closed for the New Year holiday, when the pandemic started. While supply is currently recovering, we still consider the limited impact on demand. Smartphone production for 2020 will be down 6%, but camera shipments for mobile should increase about 10% this year. Another positive trend for the mobile market is optical fingerprint implementation. Currently, high-end Android phones use this kind of technology. For 2023, we estimate optical fingerprint technology revenue to be over $1 billion.The roadmap for the automotive market is driven by camera proliferation. We’ll see 10 cameras per car and more for some high-end vehicles. Increasing demand for safety and convenience will mean more cameras per car in the future. With a strong attachment rate, the market average in automotive is around 2.0 cameras per car nowadays, and we expect the market average to reach 3.5 cameras per car in 2025. In security, Charge Coupled Device (CCD)-based cameras are nearly out of the market, as CMOS-based IP cameras are most important now.SEMI: What are current key technology trends?Liang: 3D semiconductor technology is the hot topic. CIS wafer staking technology is indeed at the center of the CIS technology race. Future applications could be AI analytics or recently developed applications on new types of CIS. So far, we have seen the introduction of variants of the CIS pixel. Global shutter (GS) and indirect Time of Flight (iToF) were recently introduced, and now direct time-of-flight (dTOF) pixels are being used in high volume. 3D semiconductor technology is a bonanza for the industry, as it allows to pack more value in a single chip. While the surface of silicon is still increasing, additional silicon is added through stacking.With COVID-19 still a problem, the endpoint for smartphones in 2020 remains uncertain. The short-term impact for CIS will be slower growth with respect to the 25% YoY of last year. The downturn in car production will be mitigated by an increased attachment rate for automotive cameras. The security market will also help maintain CIS growth.For more insights, see the following reports: Status of the MEMS Industry 2020 3D Imaging and Sensing 2020 CIS Market Monitor Q2 2020 Dimitrios Damianos is a technology and market analysts at Yole Développement covering MEMS, Sensors, Photonics and Imaging. Chenmeijing Liang is a technology and market analysts at Yole Développement covering Imaging. Serena Brischetto is senior manager of Marketing and Communications at SEMI Europe.

Thanks to developments in science and technology, artificial intelligence (AI), cloud computing, big data and other technologies have been used to establish smart healthcare systems that helps societies respond more effectively to disease outbreaks. The spread of novel coronavirus starting in late 2019 has revealed how not only traditional medicine but also Smart MedTech applications can be instrumental on the anti-epidemic front lines.To give updates on the development of Smart MedTech and how it shines during the fight against COVID-19, SEMI invited Dr. Pei-Yuan Lee, Honorary Superintendent of Show Chwan Memorial Hospital, to share with MSIG (MEMS Sensors Industry Group) and Flex-Tech members how the international community and Taiwan are bringing their best in Smart MedTech to the table and how their collective efforts are helping tackle COVID-19 challenges.Taiwan’s COVID-19 rapid screening reagents and antibody testing help curb coronavirus transmissions Taiwan’s medical community has demonstrated its prowess in responding to the COVID-19 outbreak. Using its nucleic acid extraction reagent, Taiwan Advanced Nanotech Inc. tested 128 specimens from passengers aboard the SuperStar Aquarius cruise ship in only eight hours in early February. Taiwan’s leading research institute Academia Sinica successfully synthesized the first group of monoclonal antibodies capable of recognizing the new coronavirus protein on March 8, enabling testing to be completed in 15 minutes. The College of Medicine of National Taiwan University announced on March 27 that its 30-second screening device had helped identify asymptomatic carriers. The devices detect COVID-19 in people with no symptoms if they have pulmonary infiltration and edema. It took only 14 days for Academia Sinica to successfully synthesized the first group of monoclonal antibodies capable of recognizing the new coronavirus protein. On April 22, three biomedical companies in Taiwan launched a COVID-19 test that produces results from samples of patient mucus in less than 10 minutes to greatly enhance testing speed. Once the test method is approved by the Taiwan government, it will take Taiwan’s medical strategy against COVID-19 to the next level.Artificial Intelligence: the key to upgrading traditional healthcare practicesAI is a key enabler of the transition from traditional medical practice to Smart MedTech. To help fight the COVID-19 outbreak, a National Cheng Kung University medical team developed a 30-minute coronavirus testing procedure that uses AI to read pulmonary X-ray images and automate medical records. Taiwan AI Labs leveraged AI to simulate how drug molecules combine with viruses to reduce research time by three to four years. AI ​​diagnostic technology from the Alibaba DAMO Academy (Academy for Discovery, Adventure, Momentum and Outlook) and Alibaba Cloud interprets CT images of COVID-19 patients with 96 percent accuracy in 20 seconds. AI-powered algorithms improve diagnostic test accuracy, allowing clinicians to quickly analyze scans of pulmonary lesions and quantify the severity of lung damage.Startups have also joined the fight against COVID-19. Taiwan's Internet of Things (IoT) startup iWEECARE invented the world's smallest smart thermometer patch. Heroic-Faith Medical Science launched a device that uses IoT and AI to monitor lung sounds. With Smart MedTech expected to be fertile ground for future venture investments, enterprises must find their niches in establishing new technologies in a much more systemic way. Taiwan startup Health-Faith Medical Science developed a respiratory diagnostics device that uses IoT and AI technology to monitor chest sounds in real time. Anti-epidemic technology to help fulfill smart medtech vision Many AI and big data technologies previously deployed in hospitals and healthcare systems are helping regions around the world speed their pandemic response. The United States and China have started to develop facial mask recognition systems powered by AI, while a team in the Department of Bioinformatics and Medical Engineering at Asia University has devised a facial recognition system combining IoT and AI technology with infrared thermal imaging cameras. At Johns Hopkins University, the Center for Systems Science and Engineering is using AI to create big data models that track global cases, people and traffic flow, and other variables for real-time data analysis that enables epidemiologists to more accurately predict COVID-19 transmission paths. Graphen, Inc., a New York-based provider of next-generation AI platforms, launched the world's first AI COVID-19 genetic evolutionary path analysis systems to gauge the virus’s transmission route and accelerate pandemic response. Both the United States and China are also using robots and drones to improve epidemic research and patient treatment. For the first confirmed case in the United States, robots were used to assist with medical care. In China, robots facilitate deliveries of disinfectants to makeshift hospitals built to expand the nation’s capacity to treat COVID-19 patients. While Taiwan’s robots are traditionally used for hospitality, transportation and disinfection purposes, future robotics research and development will focus more on medical applications that shift more work from medical staff to technology. With abundant technological resources and expertise, Taiwan can join hands with the rest of the world to combat the COVID-19 pandemic. Emerging technologies are pointing the way toward a new paradigm for healthcare community. Biotech, artificial intelligence, and robotics have given rise to new applications that increase virus screening accuracy and efficiency. This growing wave of technological defenses against the pandemic will become a long-term force for stability and strength in healthcare systems across the world.To get involved in SEMI Taiwan Smart MedTech Community, please contact Helen Chen, Outreach Manager, at [email protected] Huang and Winnie Chang are marketing and public relations specialists at SEMI Taiwan.

During the COVID-19 pandemic, the SEMI Global Advocacy team has been working tirelessly to ensure the microelectronics manufacturing and design supply chain is classified as an “essential business” in the United States and for similar designations in several other countries so that SEMI member companies can maintain operations. Their efforts have included direct lobbying and letters to the governors of 16 states in the U.S., 23 European countries and several European Union officials across the continent, as well as government officials in Japan, Mexico and Malaysia. The bedrock of these efforts, and the reason they have been highly effective, is that our industry enables both modern digital infrastructure and technology critical in the fight against the virus.SEMI takes immense pride in highlighting the role of our industry in providing the building blocks for innovations that improve social and economic prosperity the world over. It is never more apparent that necessity is the mother of invention than during a crisis, and the pandemic has created a diverse range of demands for technological advancements to address the myriad of challenges it presents. Our SEMI Tech Spotlight blog series highlights some of the many ways that our industry and member companies are enabling technology employed on the front lines of this fight – and that we strongly believe will ultimately help to win it. Our first piece in this series focuses on platforms enabled by big data and artificial intelligence.Fighting the Pandemic with Big Data-AI Enabled PlatformsThe COVID-19 pandemic is testing humanity in unprecedented ways, but it is also uniting us to fight this crisis with the best weapons we have. Big data and Artificial Intelligence (AI) technologies – built with microelectronic chips and systems that generate, transmit, store and analyze data – are making a profound contribution to our arsenal for this protracted war. Big data-AI technologies are enabling platforms such as data analytics, robotics, augmented/virtual reality (AR/VR), 3D printing, and others that are already being applied to address many facets of this crisis.Big Data and Analytics Inform Policy In the fight against COVID-19, data analytics platforms are being used first and foremost to slow the rapid spread and to inform policy decisions. This requires analysis of massive amounts of data about public health and travel, often using AI algorithms. The state of California, for example, is partnering with companies such as BlueDot, Esri and Facebook to build a software platform that uses smartphones and location intelligence to track people’s movement and predict hospital needs. Taiwan owes its considerable success in limiting the spread of the virus to the extensive use of big data analytics for identifying and tracking carriers. Google and Apple are driving a joint effort that connects Bluetooth with their popular iOS and Android platforms to trace contacts of infected people. India has developed Aarogya Setu, a mobile app based on Bluetooth and location-mapping platforms, designed to alert citizens if they have crossed paths with another app user who has tested positive for the virus. This app was launched in 11 languages, and despite being entirely voluntary, it was downloaded by 50 million people in 13 days, making it the world’s fastest-ever to reach that number. Such contact-tracing apps, now being rolled out in at least 26 countries, carry inherent privacy and security challenges due to the sensitive data they access. While mitigation strategies like strict data anonymity and opt-in protocols are being implemented, these will need to be refined over time.Robotics Protect Frontline SoldiersToday’s robust robotics platforms are enabled by huge amounts of data from sensors and guidance from predictive AI algorithms. These robots can learn on the job, adapt to the environment, and work safely with humans. In this pandemic, they are perfect for minimizing human interaction with infectious environments. Companies around the world such as Boston Dynamics, Akara Robotics, UBTECH Robotics and CloudMinds have already deployed robots on the front lines of this war to assess patient health, disinfect hospital surfaces, and help health workers with Personal Protective Equipment (PPE).Robot drones are also delivering blood and other lab samples. For example, WakeMed hospitals in North Carolina launched the first drone delivery program approved by the U.S. Federal Aviation Administration with Matternet drones operated by UPS; while Terra Drone from Japan executed similar tasks in the hard-hit Wuhan province of China.3D Printing Speeds ManufacturingBig data-AI technologies enable 3D printing platforms by providing accurate 3D models for optimized designs and defect-free manufacturing. Low-cost, fast-cycle-time 3D printing has helped to alleviate at least some of the medical equipment shortages. For example, the U.S. Food and Drug Administration (FDA) has approved the first 3D-printed “Stopgap Face Mask” for liquid barrier protection from the SARS-CoV-2 coronavirus for healthcare workers. The U.S. Veterans Health Administration has developed this in collaboration with America Makes using an open-source database – the 3D Print Exchange from the National Institutes of Health. In another example, Formlabs worked with Northwell Health, New York’s largest healthcare provider, and University of South Florida (USF) Health to develop and test a nasal swab prototype over just one weekend, and it is now producing up to 150,000 test swabs daily. Prisma Health in South Carolina received emergency FDA authorization for VESper, a 3D printed device that allows a single ventilator to support two patients, and possibly up to four.Telehealth Becomes a “New Normal”Telehealth is not a new concept but is much enhanced by today’s microelectronics platforms that can collect and transmit rich datasets with very low latency. Further, rapid data analysis is increasingly supported by AI systems. The requirement for social distancing makes telehealth a perfect solution for many healthcare consultations. U.S. government data indicates that the daily average of telehealth claims from private insurance for upper respiratory infections increased nearly 12 times over the previous month from March 14 to April 1. Similarly, Teladoc Health coordinated 100,000 patient “televisits” in the week of March 8 – a 50 percent spike over the previous week, taking pressure off the healthcare system. The next generation of telehealth is likely to use AR/VR platforms, which use even richer datasets and AI to improve the accuracy and predictive capability of their underlying models. Consequently, these platforms can provide more realistic experiences and improved outcomes. At least 11 states in the U.S. are already working with AR/VR companies such as XRHealth and AppliedVR for primary care and many medical specialties. Accelerating the Search for a Vaccine or TreatmentThe way out of this pandemic depends on swiftly finding a vaccine and a treatment, ideally by fast-tracking the traditionally slow drug development process. Big data-AI technologies are at the forefront of such efforts globally, often using the most powerful supercomputers available. For example, researchers at the University of California, San Diego (UCSD) are using the Frontera supercomputer to build a complete model of the SARS-CoV-2 coronavirus envelope – a formidable task, requiring analysis of data from 200 million atoms and interactions between them. Researchers at Argonne National Laboratory are combining AI with physics-based models to search for a molecule that might disrupt the activity of the virus, a precursor to finding a treatment. Also, several companies around the globe such as BenevolentAI (UK), Gero (Singapore), Innoplexus (Germany-India), and Insilico Medicine (US-Hong Kong) are using AI platforms to accelerate the search for a solution. ConclusionUltimately, the success of technology is not measured by the number of bits and bytes or by the speed of algorithms. It is measured by every janitor who did not have to clean a hazardous surface because a robot did, by every doctor and nurse protected by a 3D-printed mask, and by every person whose life may be saved by the accelerated discovery of a vaccine or treatment. Big data-AI technologies, and the platforms they enable, are just coming of age – they give us hope that as they evolve in the future, we can use them to build a more resilient society and economy.Note/Disclaimer: The examples cited above are purely for illustration – they are neither comprehensive, nor intended to endorse any particular product or solution.The SEMI Smart Data AI initiative helps members realize full value in the intelligent future enabled by Big Data and Artificial Intelligence – including the large revenue upside, and the transformational potential for operational and supply-chain efficiency. For more information on the initiative, contact Pushkar Apte at [email protected] Manocha is President and CEO of SEMI. Pushkar P. Apte, Ph.D., is the Strategic Technology Advisor for the Smart Data AI Initiative at SEMI.