electronics manufacturing

In a recent project funded by FlexTech, a SEMI Technology Community, Goleta, Calif.-based ACI Materials developed alchemy conductive inks, optimized to enable the printing of finer pitches on flexible and 3D printed circuit structures than possible with conventional polymer thick film inks.

In this article, several experts weigh in on considerations for developing the smart manufacturing workforce that will help to alleviate the U.S. semiconductor manufacturing talent shortage, even as multiple, data-intensive $1 billion to $10 billion fabrication facilities come online.

As a leading analyst covering the electronic system design segment, Laurie Balch is well steeped in identifying and analyzing technology trends and forecasting new market opportunities. Laurie is now President and Research Director at Pedestal Research.

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.

The state of Penang, nestled along the northwest coast of Peninsular Malaysia, needs no introduction in the global electronics manufacturing sector. Despite its diminutive stature with just over 1,000 square kilometers of land area and a 1.8 million-strong population, Penang commanded an estimated 5% of global semiconductor exports in 2019, according to data compiled from the Department of Statistics Malaysia (DOSM) and UN Comtrade. The State’s transformation, from a traditional seaport economy into the Silicon Valley of the East, began in the 1970s, when the establishment of Malaysia’s first free trade zone in the State drew key investments from eight Multinational Corporations (MNCs). These pioneering investors – Intel Corporation, Hewlett Packard (now Keysight Technologies and Agilent Technologies), Robert Bosch, AMD, Litronix (now Osram Opto Semiconductors), Hitachi (now Renesas), Clarion and National Semiconductor[1] – sparked the development of a robust ecosystem of ancillary industries, which formed a foundation for the State’s rise as a prominent, offsite manufacturing hub. Today, Penang houses more than 350 MNCs that are supported by over 3,000 manufacturing-related SMEs. As Penang flourished as a vibrant, regional E E manufacturing hub, the local talent pool steadily accumulated a wealth of business intelligence and technical experience, enabling the robust supply chain to evolve in tandem with technology megatrends. This, in turn, enabled the State to focus on pursuing investments that have propelled the industry up the value chain, away from its beginnings as a low-cost manufacturing hub. Consequently, Penang has seen a proliferation of upstream technology-related investments in high value-added functions in recent years, ranging from research and development (R D), design and knowledge-based solutions, and downstream advanced manufacturing and testing to global business service (GBS) and Centre of Excellence (CoE) activities. Penang’s growing significance in the global E E value chain is demonstrated by its steady and resilient export performance in recent years. From 2014 to 2019, the State’s E E exports grew at a compounded annual rate (CAGR) of 12% to reach RM210 billion (US$51 billion). It has emerged as a hub for professional, scientific and controlling instruments (including medical technology), with exports of these products growing at a 5-year CAGR of 15% to reach RM23 billion (US$6 billion) in 2019. E E products, alongside professional, scientific and controlling instruments, collectively contributed between 77% and 82% of Penang’s total annual exports since 2014, and accounted for 50% of Malaysia’s exports in these two segments during the period. More impressively, despite the disruptions from the COVID-19 pandemic, Penang’s total exports continued to rise in 2020, growing 7% year-on-year to RM310 billion (US$75 billion), and a further 14% year-on-year in January and February 2021, driven by strong global demand for semiconductors. Shaping up as the destination of choice for advanced manufacturing investments As part of efforts to move Penang’s industry up the value chain, the State government has placed emphasis on attracting companies with strong commitments in implementing Industry 4.0 and sustainable investing. These efforts have yielded positive results, with the state having gained traction as a hub for advanced manufacturing investments. This is evidenced by the rising trend in investments per new job creation, which saw a six-fold jump from 2012 to 2020, as well as the number of global heavyweights announcing new investments as well as expansions of existing facilities in the State in 2019 and 2020. Penang attracted RM31 billion (US$7.5 billion) in approved direct manufacturing investment inflows in 2019 and 2020, 88% of which involved investments into the E E, equipment and medical technology industries. Prominent new investments included those from Lam Research, Bosch Group, Ultra Clean Holdings, Dexcom as well as Smith+Nephew. Together with planned expansions by a number of existing MNCs in Penang, these new investments, which are on track to commence operations between 2021 and 2023, are poised to bring Penang’s industry to greater heights and further integrate the State into the global value chain. Recent Notable Direct Manufacturing Investments in Penang Source: InvestPenang and respective companies Penang’s conducive business environment nurtures successful homegrown technology companies Penang’s conducive business environment has not only proven successful in attracting foreign direct investments (FDIs), but also successfully nurtured local E E success stories of locally employed engineers turned technopreneurs, who have founded and built companies that have successfully grown to become internationally renowned in their own right. These homegrown E E companies play crucial roles in the ecosystem, particularly in the areas of automated test equipment (ATE), automation, outsourced semiconductor assembly and testing (OSAT) services, electronics manufacturing services (EMS), precision engineering and tooling. The past five years have also seen the emergence of young, fast-growing Penang-based companies such as Experior, Oppstar Technology and Skyechip, which provide IC design and IC test design services to MNC clients globally. Public-private partnerships cultivate Penang’s talent development roadmap The state is cognisant that the development of a robust and skilled talent pool is imperative to support the growth of strategic industries in Penang. Strong public-private partnerships with concerted efforts in supporting talent development are key to Penang’s continued success. Toward this end, the State government has backed Penang Skills Development Centre’s (PSDC) industry-led training and education efforts, which have helped train over 200,000 of workers to support the industry’s needs since 1989. The State has also coordinated collaboration for industries to provide input to local institutions of higher learning on the relevance of the institutions’ courses, and rallied the industry to support State-run scholarships (Penang Future Foundation) and STEM initiatives. Holistic initiatives to make Penang a world-class investment destination for global frontier companies The dynamics of the global value chain, especially for the technology sector, have evolved rapidly since 2018, particularly amid the complex confluence of trade protectionism, COVID-19 pandemic-driven issues and disruptive technologies. The State government believes that strong, geographically localised industry clusters could help companies mitigate the risks of supply chain disruptions, in addition to improving companies’ time-to-market at a lower cost. To further increase Penang’s attractiveness for high quality investments, the State is focusing on three key strategies: Extending its competitive edge in advanced manufacturing, further strengthening Penang’s industry clusters, which include expediting SMEs’ Industry 4.0 transformation journey, and nurturing more homegrown companies to penetrate the global supply chain Embarking on a continuous drive to develop and recruit talent to the State, as well as cultivate the younger generation’s interest in STEM Enhancing Penang’s liveability with a strong focus on making Penang a smart and green city The State government is committed to continue developing Penang in a holistic manner, with the aim of creating a vibrant business and investment destination with a robust and sustainable economy and high standard of living, creating a conducive environment to “work, live, learn, play and invest.” About InvestPenang InvestPenang is the Penang State Government’s principal agency for promotion of investment. Its objectives are to develop and sustain Penang’s economy by enhancing and continuously supporting business activities in the State through foreign and local investments, including spawning viable new growth centres. To realize its objectives, InvestPenang also runs initiatives like the SMART Penang Centre (providing assistance to SMEs), Penang CAT Centre (for talent attraction and retention) and i4.0 seed fund (a catalyst for the start-up ecosystem). For more information, contact [email protected]. InvestPenang also works closely with various industry associations, including SEMI, to promote Penang’s supply chain and E E ecosystem. InvestPenang is delighted to have collaborated with SEMI on numerous occasions since 2015 and endeavours to sustain the partnership in the years to come, including for the SEMICON SEA 2022 exposition to be held in Penang. [1] No longer present in Penang following a corporate M A exercise.

Introduction Automated production in electronics manufacturing can produce high-quality products, but it might lead to a particular failure without human interventions. With the rapid technology development, such as the Industrial Internet of Things (IIoT), big data analysis, cloud computing, artificial intelligence (AI), many manufacturing processes can be more intelligent, and Industry 4.0 can then be realized in the near future[1]. Smart manufacturing adopts real-time decision-making based on operational and inspectional data and integrates the entire manufacturing process as a unified framework. Then, the future manufacturing process transforms cyber-physical systems digitally and responds to any uncertain situations proactively while ensuring higher efficiency. In Surface Mount Assembly (SMA) lines, equipment status and quality data can be collected via IIoT technology. Data-driven solutions, such as AI and machine learning algorithms, can be applied to diagnose abnormal defects and adjust optimal machine parameters in response to unexpected changes/situations during production. Collaborating with various SMT industry partners, the research team at the State University of New York at Binghamton (aka Binghamton University) developed a novel framework based on AI-based closed-loop feedback control and parameter optimization to implement a smart manufacturing solution in the PCB assembly for yield and throughput improvement. This AI-based framework could provide a potential road map for data-driven process control in SMA. Machine Intelligence in SMA Each SMA process has a critical effect on the final PCB product quality and throughput. Notably, the solder printing process is a critical operation because over 60% of the PCB assembly soldering defects can be traced back to this stage. An inadequate volume of solder paste transferred to any PCB pad is a printing fault, which leads to board failure and substantial reworking and repair costs. The pick and place (P P) process is the highest cost procedure, including expensive machine investment and extended production time. In the soldering reflow process (SRP), the reflow oven temperature and other related settings determine the solder joints' quality and reliability. Hence, multiple inspection machines in the SMA processes have been introduced, including solder paste inspection (SPI) and automated optical inspection (AOI) machines. Particularly, two independent AOIs could be employed to detect the components' defects before and after SRP. Because many electronics components become small-scale (e.g., ), more assembly-related failures are often observed in recent SMA processes. The Smart Electronics Manufacturing Laboratory (SEML) at Binghamton University is fully equipped with two solder paste printers, two chip-mounters, and a reflow oven along with SPI and AOI machines. The research team tested more than 8,000 PCB at SEML. The results show that numerical methods based only on physical properties might have practical limitations in explaining small-scale components' behavioral patterns. It might be caused by unknown environmental factors (e.g., temperature and humidity), machine calibration, measurement accuracies, vibrations, etc., which could have influenced the quality of the SMA outcomes. However, recent research shows that AI-based methods can increase product quality up to 35%, reduce scrap rates, and optimize fab operations in semiconductor manufacturing, compared to traditional approaches[2]. It implies that a data-driven intelligent SMA process control has the potential to advance SMA processes. The goal of the smart SMA is to maintain optimized settings in both offline and online scenarios. The AI and data analytics solution can optimize all SMA process parameters before production (i.e., offline control) and during production (i.e., online control). The overall schematic of the AI-based closed-loop feedback control framework is illustrated in Figure 2. Intelligent SMA Modules In the solder printing process, four machine intelligence modules are considered: Printing advising module (PAM) Printing optimization module (POM) Printing diagnosis module (PDM) Dynamic stencil cleaning process control (CPC) Figure 2. A schematic diagram of the AI-based closed-loop feedback platform Figure 3. PAM effectiveness over customer’s best-known printing parameter setting PAM aims to recommend the ideal initial setting of the printer critical parameters, such as printing speed, printing pressure, and separation speed, using hybrid machine learning and heuristics optimization techniques[3]. As a case study, the research team validated the PAM's performance with an automotive PCB testbed and compared the printing results to the best-known printing parameters. The experimental results show that PAM can achieve over 50% higher Cpk (i.e., process capability index, as shown in Figure 3). POM optimizes printing parameters in real-time by monitoring printing quality and fine-tuning offset and process parameters for adapting to dynamic conditions[4]. The experimental results show that POM achieves more than 30% production quality improvement in terms of the Cpk by adjusting printing parameters compared to the offline control. PDM provides anomaly detection and diagnosis of the potential printing failure cases to improve process quality and reduce downtime[5]. The experimental results show that the PDM can achieve more than 87% accuracy in predicting different types of defects, improper printer hardware issues in the board support, squeegee, paste conditions, etc. The CPC uses the SPI information to estimate residue buildup level on the stencil undersurface and assess the stencil cleaning profile and cycle control, as illustrated in Figure 4[6]. Upon the implementation of CPC, it is expected that the robustness of the printing quality and the Cpk can be improved by 34% and 10%, respectively, compared to the best-known cleaning parameters using in the production line. Figure 4. Smart residue buildup prediction for stencil cleaning operation control During the P P procedure, the mounter optimization module (MOM) and the mounter diagnosis module (MDM) can be applied as a machine's automation process while optimizing the P P machine's parameters. By utilizing self-alignment effects appropriately, MOM identifies the optimal placement position by predicting the component's post-reflow positions based on the data collected by SPI, Pre-AOI, and Post-AOI machines, as shown in Figure 2. MOM also offers the placement positions adaptively during active production. In the MOM framework, multiple dynamic placement options are first generated based on the solder paste offset information. The components' final offsets in both x and y directions are predicted by a hybrid AI model that stacks on the k-nearest neighbor regression and the gradient boosting regression models. The optimal placement, which has the minimum predicted post-reflow misalignment, can be identified by MOM. The experimental results show that MOM can decrease 18% of the final misalignments compared to a conventional P P placement method (i.e., placing a component on the pad center). MDM is a prescriptive and predictive maintenance method that uses P P machine operational and AOI inspection data to trace back the root causes of P P defects and prevent future failure. MDM can achieve an accuracy of 84.50% in identifying the known root causes of certain defects, such as improper nozzle size, parts' contamination, and feeder problem. It shows that different mounting defects can be detected and classified automatically when the abnormality is detected through AI-based diagnosis algorithms. Figure 5. The illustration of the optimal placement position in the MOM One of the reflow oven issues to be addressed is to find the optimal reflow oven temperature settings, which would affect the final quality of the PCB products. Solder paste manufacturers usually provide a target profile based on the solder paste composition's physical properties, and solder joint temperature is required to meet the given profile. Hence, reflow engineers should fine-tune reflow oven temperature manually to ensure a thermal profile outcome from the reflow oven to correctly meet a target profile, requiring substantial cost and effort. The research team proposes an automated reflow recipe optimization model based on the PCB thermal profile and its recipe. Figure 6. Optimized thermal recipe and thermal profile First, the initial recipe collects the data for the prediction model and identifies the relationship between the thermal profile and the corresponding recipe. Then, an AI-based model is developed to predict the thermal profile based on the input recipe. Compared to traditional methods, the AI-based method generates an optimal reflow oven recipe to minimize the gap between the predicted temperature and the given profile. As a result, the AI-based prediction model allows us to achieve promising results, such as 97% of fitness in the given profile temperature curve within one hour of processing time. The proposed model has other significant advantages, such as saving time, labor, and materials. It enhances the degree of automation of the PCB reflow process. In the future, data from multiple inspection machines will be integrated so that the reflow optimization process is fully automated and generates more reliable results. Summary and Conclusion The small-scale electronics products make the SMA processes much more complicated to maintain high-quality PCB products, and theoretical interpretations of the SMA processes can be challenging due to many uncertain factors. With the help of AI and big data collected from various inspectional operations, SMA processes can be intelligent and flexible in response to dynamic environmental situations. While retaining the optimal control parameters throughout the SMA processes, the final PCB product quality can be enhanced while maintaining the designed throughput. Automated and smart systems bring about the opportunity to next level of electronics manufacturing, which utilizes the data and information from the end-users through edge/cloud computing and fastens the customized product manufacturing with increasing efficiency for high-mix/low-volume manufacturing. Also, it can increase verities of design and fasten the delivery time. About the Author Prof. Sang Won Yoon is a recipient of the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2019 and a highly successful researcher who leads many productive long-term industry collaborations. Prof. Yoon received his doctoral degree in School of Industrial Engineering at Purdue University, and he joined the faculty of the Watson School in the Department of Systems Science and Industrial Engineering at State University of New York at Binghamton in 2010. Prof. Yoon has been studying how to extract useful insights from expanding data sets to support intelligent decision-making processes. His research not only resides in better understanding large-scale data set by using statistical learning methodologies, but also leverages optimization, soft computing, simulation, and complex theories with conventional machine learning algorithms. As a result, Prof. Yoon has published in over 130 internationally renowned journals and conference proceedings. He was also a member of the Data Science Transdisciplinary Area of Excellence (TAE) initiative and is an active member of the Health Sciences TAE at his institution. The author recognizes the following for their assistance with this article: Daehan Won, [email protected], Assistant professor Jingxi He, [email protected], Ph.D. candidate Shrouq M. Alelaumi, [email protected], Ph.D. candidate Yuanyuan Li, [email protected], Ph.D. candidate Yuqiao Cen, [email protected], Ph.D. candidate References ​​​​​​​[1] Qi, Q., and Tao, F., 2018. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, pp.3585-3593. [2] 10 Ways machine learning is revolutionizing manufacturing in 2019. https://www.forbes.com/sites/louiscolumbus/2019/08/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2019/?sh=7cd2e9e22b40. [3] Khader, N. and Yoon, S.W., 2018. Stencil printing process optimization to control solder paste volume transfer efficiency. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(9), pp.1686-1694. [4] Lu, H., Wang, H., Yoon, S.W. and Won, D., 2019. Real-Time stencil printing optimization using a hybrid multi-layer online sequential extreme learning and evolutionary search approach. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9(12), pp.2490-2498. [5] Alelaumi, S., Wang, H., Lu, H. and Yoon, S.W., 2020. A Predictive Abnormality Detection Model Using Ensemble Learning in Stencil Printing Process. IEEE Transactions on Components, Packaging and Manufacturing Technology, 10(9), pp.1560-1568. [6] Alelaumi, S., Khader, N., He, J., Lam, S. and Yoon, S.W., 2021. Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network. Robotics and Computer-Integrated Manufacturing, 68, p.102041.

Linx Consulting and Hilltop Economics continue to monitor how the global economy impacts the electronic materials supply chain. Amidst the recent economic and revenue results releases, we have generated a series of potential scenarios for the next few years. These scenarios are based around sales of silicon wafers expressed in millions of square inches (MSI). Our work develops a multiyear forecast from the historic record of the SEMI-reported MSI demand by developing an econometric relationship with underlying demand drivers. Using this methodology, Linx Consulting and Hilltop Economics have introduced the following three silicon demand forecast scenarios: V-shaped global recession consistent with severe COVID-19 impact followed by a sharp economic rebound. Probability of approximately 40%. V-shaped global recession but with business and consumer behavior differing from the past recession in that there is much more aggressive spending on technology goods that softens the impact for semiconductors in 2020. Probability of approximately 25%. An extended COVID-19 impact developing into a U- or L-shaped global recession with an economic rebound delayed for several years. Probability of approximately 35%. In the few months since coronavirus hit the world, the economic prognosis for all major economies has worsened dramatically, although forecasts remain speculative given the rapid rate of change in the political and economic environment. The forecast changes in GDP since February 2020 of the G7 nations vary from -5.9% for Japan to -10.2% for Italy. These changes are closely linked to unprecedented declines in employment, consumer demand and industrial investment – all key drivers for wafer area demand. This leads us to believe there will be a significant reduction in wafer demand as these economic factors feed through the supply chain.Other leading indicators show dramatic drops in the global and regional economies taking effect at an unprecedented pace. These indicators have a loose predictive relationship for silicon wafer consumption and portend a rapid drop in demand.The demand picture for the semiconductor supply chain (be it wafers, materials, consumables or devices) is thus gloomy, and our models are currently showing Q2 to Q3 2020 reductions in MSI demand of between -11% and -28% depending on the scenario.In marked contrast to this depressing economic picture, the indications from the end-to-end semiconductor supply chain continue to be much more positive. Demand for silicon reported by SEMI increased in Q1 2020 by close to 3% from Q4 2019, while results from materials supply companies vary from slightly negative to record-breaking growth rates through the first three to four months of 2020. Added to this, reported revenues from WSTS for Q1 2020 ticked up 6.2% versus the prior year and the three large foundries in Taiwan and China showed continued growth of Q1 wafer area shipments and a 32.3% growth versus Q1 2019.Revenue and demand reports from leading device manufacturers remain on trend from 2019 with no indication of a precipitous change. Anecdotal reports of strong technology equipment demand to support people working from home and demand for medical devices in response to the pandemic can be substantiated somewhat by demand data although not convincingly.Reports from materials supply companies indicate that factories continue to be fully utilized, having been designated essential businesses, and that safety measures implemented against infection are largely effective.There are some indications of caution, however. The major public silicon wafer suppliers saw a 4% drop in revenues in Q1 over Q4, despite the reported strength in silicon area shipments from SEMI, indicating either ASP declines or some inventory effects.We are advising clients supplying materials into the wafer fabs and packaging supply chains to develop contingency plans for a sharp decline in product demand of as much as 28%, which may bounce back rapidly to 2019 levels or higher in early 2021. However, companies should also be vigilant of a slower than hoped for return to previous activity levels if the effects of the pandemic continue for an extended period.For further information please contact Mark Thirsk at +1 774-245-0959 or on [email protected] in engaging with the electronic materials supply chain? The Electronic Materials Group (EMG) is a SEMI technology community representing SEMI member companies that provide substrates, polymers, metals, organic and inorganic materials, chemicals, and gases developed for electronics manufacturing. Linx Consulting is a longtime member and supporter of the SEMI Electronic Materials Group.Mark Thirsk is managing partner at Linx Consulting. Duncan Meldrum is president of Hilltop Economics.

4 Key Takeaways from SEMI Taiwan Member ForumThe rapid development of artificial intelligence (AI) has accelerated the digital transformation in various industries and has now fused with Internet of Things (IoT) to exploit the value of both technologies in reshaping the electronics industry value chain. As it emerges from the shadows of its parent technologies, AIoT is giving rise to new opportunities in manufacturing, healthcare, transportation, and even energy. AIoT is fast rising in prominence as an enabler of key electronics manufacturing process improvements and the creation of add-on value to existing products – both critical to the success of many businesses.SEMI and the SEMI MEMS Sensors Industry Group (SEMI-MSIG) held a technical forum on smart sensing and its applications in AI and AIoT, inviting renowned experts in sensors and edge computing to share in-depth insights into the latest AIoT technologies and applications with more than 100 industry professionals in research and development, marketing and sales. Here are four key takeaways from the SEMI Taiwan member forum.1. Steady Growth for Global Sensors MarketThe global sensors market’s steady growth is expected to expand at a CAGR of 6.6 percent from 2017 to 2023, with Asia driving the biggest gains and automotive leading the segments – including healthcare and education – with the strongest growth. Automotive alone is expected to reach US$34 billion in 2023.2. Integration Critical to MEMS Sensors DesignsWith AI booming, MEMS sensor designs need to drive toward greater integration —not only integrating data collection with sensors, but also streamlining data processing on the backend – making 3D models of today’s MEMS mechanical designs critical. The differences between 3D and entrenched 2D models are dramatic, elevating the importance of specifying manufacturing steps in MEMS designs. As new sensors and applications continue to emerge, companies that develop the most powerful integrated designs will win. 3. Growth of Smart Voice-Control Applications to ExplodeAIoT is also accelerating the development of smart voice-control applications and the rise of new related business opportunities. Just 50 million voice-controlled devices shipped worldwide in 2017, a number predicted to swell to 436 million in 2021 with smart home devices such as set-top boxes and smart TVs the major growth drivers.4. AIoT Eyed to Make Human-Robot Collaboration SafeSafety is an essential feature for human-robot collaboration. Tactile sensing technologies give robots a layer of “skin” with capabilities rivaling human touch. To ensure humans and robots work together safely in work environments, sensors on this layer of skin are concentrated – less than 8mm apart, equivalent to the width of a human finger, with a response time of less than 5ms on contact. More than 4 million robots worldwide are expected to be upgraded with these sensing technologies and are on track for deployment in pilot plants in the next three years.SEMI-MSIG is committed to strengthening connections across all sectors in the MEMS and sensors supply chain, working closely with the industry to accelerate the development of related technologies and applications in both mature and emerging markets. In addition, SEMI-MSIG hosts regular events to inspire business opportunities and technology exchange for innovative applications, while enhancing the visibility of members among global customers and partners to help them forge new partnerships. To join the group, contact SEMI Taiwan’s Helen Chen at [email protected] Yi is a marketing specialist at SEMI Taiwan.