cloud computing

The European semiconductor ecosystem continues to evolve, driven by the ambitions outlined in the EU Chips Act. With goals to strengthen Europe’s technological leadership and double its semiconductor manufacturing market share to 20% by 2030, collaboration across the value chain is imperative. Heterogeneous Integration for Connectivity and Sustainability (HiCONNECTS), a Horizon Europe-funded project, exemplifies this collaborative spirit. The initiative aims to develop next-generation electronic components and systems using advanced heterogeneous integration core technology solutions.The HiCONNECTS consortium, comprising 65 partners with diverse expertise, is addressing key societal and industrial challenges. These efforts focus on advancing core technology solutions for energy-efficient, high-performance wireless and wired cloud and edge computing, as well as automotive radar systems.“Collaborating with 65 partners is no small feat—it’s akin to orchestrating a complex IT network,” says Ilan Englard, Coordinator of the HiCONNECTS project. “We streamline progress by creating local networks of partners, all interconnected through a central management framework of tasks, work packages, and coordination. Such large consortia form intricate systems where complexity fosters innovation, often leading to surprising and transformative outcomes.” As the three-year project progresses, HiCONNECTS is working to establish pilot lines focused on key areas:RF Electronic Heterogeneous IntegrationPhotonic Components for Heterogeneous IntegrationAdvanced Packaging for Heterogeneous IntegrationThese pilot lines, led by organizations such as the Ferdinand Braun Institute and imec, will develop systems and modules through advanced equipment development, manufacturing optimization, and integration of electronic and photonic components. Validation of equipment in integrated process flows will further enhance the heterogeneous integration landscape.Now in its third year, HiCONNECTS continues to welcome new members. This inclusiveness underscores the project’s flexibility and its commitment to incorporating fresh perspectives as new trends and challenges emerge. At the 12-month consortium meeting in Catania last February, Arbonaut was unanimously inducted to contribute to the forest fire use case, further expanding the project’s scope.“The upcoming months are critical, as we move closer to delivering modules, systems, and demonstrators,” says Englard. “Our goal is to heterogeneously integrate the next generation of RF, electronic, and photonic components into networking, telecom, and radar systems, with support from module and equipment makers.”HiCONNECTS members at the 12-month consortium meeting in Catania, February 2024As this ambitious work progresses, sharing project results and achievements remains a top priority for the consortium to ensure meaningful social, political, and economic impact. By drawing attention to the results of the project, the consortium enhances the visibility, comprehension, and implementation of these advancements. Recently, four partners—Excillum, TNO, SANLAB, and Centria University of Applied Sciences—participated in a webinar titled “Heterogeneous Integration for Future High Speed Communication,” organized by SEMI Europe. The webinar is now available on demand for viewers worldwide.The significance of HiCONNECTS was further highlighted at SEMICON Europa 2024, where seven consortium members presented progress on topics ranging from advanced packaging to photonic integration. At the TechARENA, representatives from SEMI Europe, Excillum, Centria, Arbonaut, AT S, imec, and Applied Materials showcased the project’s contributions to the semiconductor ecosystem. “I was thrilled to present at the TECHArena and engage with the HiConnects partners,” said Julius Hållstedt, Head of segment - Semi Electronics, Excillum. “I especially appreciated the high attendance at my talk, which validated the strong interest in X-ray solutions for semiconductor applications. The insightful discussions at the SEMICON Europa exhibition and advanced packaging conference was a rewarding bonus.”HiCONNECTS Speakers at SEMICON Europa 2024By disseminating research and breakthroughs across various channels, such as publications, webinars, and conferences, HiCONNECTS is promoting knowledge sharing and fostering collaboration across the semiconductor ecosystem. This openness accelerates the adoption of new technologies, ensuring that European industry players remain at the forefront of critical advancements. Furthermore, sharing these results strengthens Europe’s position as a hub for cutting-edge research and development, driving both economic growth and technological leadership on the global stage.SEMI Europe is proud to be a consortium member of HiCONNECTS under the Chips Joint Undertaking (Chips JU), which is funded by the EU Horizon Europe program and supported by numerous countries, including Austria, Italy, Germany, and Sweden.About HiCONNECTS:HiCONNECTS (Heterogeneous Integration for Connectivity and Sustainability) is a three-year project bringing together 65 partners to develop sustainable, energy-efficient cloud and edge computing platforms. The project focuses on high-performance computing, storage infrastructure, network interfaces, and real-time analysis of IoT sensors and big data.Kartikey Srivastava is Senior Specialist – Communications at SEMI Europe.

SEMICON Europa 2022 (Munich, 15-17 November) attendees gathered at the Executive Forum for the Opening Ceremony on day one to hear from industry leaders and gain insights into the latest market and technology trends.

Founded in 2004, S2C’s worldwide customer base uses its desktop and enterprise field programmable gate array (FPGA) prototyping tools to verify system on chip (SoC) and application-specific integrated circuit (ASIC) designs will work as intended.

For the semiconductor industry to flourish, all chip ecosystems must embrace closer ties, greater openness, and more flexibility to co-innovate solutions in today’s rapidly unfolding global digital transformation.

The year 1981 was notable for a variety of technology breakthroughs. Few could have predicted just how dramatic an impact the global electronics industry would have in the following 40 years, as it now touches almost every aspect of our daily lives.

AEM Holdings Ltd, a Singapore-based multinational corporation, is listed in Forbes Asia’s 200 Best Under A Billion 2019 and 2020 spotlighting small and midsized companies in the Asia-Pacific region with sales under $1 billion. AEM clinched the Singapore Business Review Technology Excellence Award 2020 for Analytics-Semiconductor and the Singapore Business Awards Enterprise Award 2019/2020. These achievements are testament to AEM’s vision and innovation and the company’s contributions to the increasingly complex testing of chips in a rapidly evolving technological world. I spoke with AEM CEO Chandran Nair, a new Regional Advisory Board (RAB) member of SEMI Southeast Asia, about the company’s intelligent test and handling solutions, its role in digital transformation, the company’s key role in the smart manufacturing movement and the growth prospects for Singapore’s electronics sector. SEMI: AEM’s application-specific, intelligent system test and handling solutions for semiconductor and electronics companies serve the advanced computing, 5G and AI markets. How do you differentiate your solutions from those offered by competitors? Nair: A key differentiation for AEM is that we work closely with our customers to develop application-specific integrated test and handling solutions that meet their needs in a scalable manner from lab to production. We offer our customers customized, full-stack test and handling solutions that give them the agility to accelerate their delivery cycles and enhance product quality. Over the years, AEM has developed and acquired world-class technologies in instrumentation, test, automation, robotics, optical inspection, high-end thermal control, and software. These technology pillars, along with our deep know-how to customize test and handling solutions using the technology pillars as a platform, enable AEM to meet the fast-changing needs of our customers faced with the challenges of testing heterogeneous and complex devices. In addition to investing in technology, AEM has also invested in delivering application-specific solutions to meet customer demand. Our recently announced acquisition of CEI with its manufacturing capabilities in Vietnam and its specialization in low-volume, high-mix manufacturing increases our geographical reach and our ability to quickly turn application-specific test and handling solutions to be deployed. We have a unique and differentiated approach that enables our customers to test high-performance computing devices, automotive devices, and mobility devices with maximum test coverage, cost-effectively, in a manufacturing environment. Our experience in serving the high-performance computing market that traditionally drives advancements in thermal control also puts us at the forefront of delivering comprehensive thermal management, vision, and deep automation and test solutions for the computing, automotive, and mobility markets. AEM also has a strong instrumentation portfolio, including high-density digital instruments and mixed-signal and protocol-aware instrumentation that is well-suited for ATE solutions for SoC, high-power devices, and CMOS image sensors. Over the last few years, we have also established leadership positions in developing and deploying application-specific test solutions for MEMS devices and offering wafer and frame probing stations suitable for R D, wafer sort, and final test. We form strong partnerships with our customers, provide them with end-to-end support in product development, and take them through the entire life cycle process from concept to mass production. Chandran Nair and Goh Meng Klang, vice president of operations, at the AEM manufacturing site in Singapore. (Photo credit: AEM) SEMI: Digital transformation is powering strong growth of advanced computing, 5G and AI. Will AEM be expanding its AEM manufacturing plants in China, Malaysia and Singapore to meet rising demand for these technologies in the coming years? Nair: In regards to manufacturing, AEM currently has manufacturing facilities in Singapore, Malaysia, the U.S., Finland, and China. With our recently announced acquisition of CEI, we will add manufacturing capability in Vietnam and Indonesia. AEM will continue to expand manufacturing appropriately to give our customers cost-effective solutions while maintaining our proven track record of delivering on time and scaling rapidly in times of crises like the pandemic or geopolitical disruptions. As for advanced technologies, the three key factors that will bring the full potential of 5G to fruition are 1) cost-effective, high-powered processing devices at the edge, 2) easy access to high-bandwidth communications, and 3) cost-effective sensor technology. Semiconductors are the primary drivers of these three key success factors. As devices become more complex and our reliance on semiconductor-powered devices in all aspects of our lives deepens exponentially to include mission-critical applications, AEM’s role is to ensure that our customers' electronic and semiconductor devices are shipped thoroughly tested, safe to use, and highly reliable. It is imperative that, as a testing company, we find innovative ways to help our customers test their products with maximum coverage and minimum cost. To do this, we are focusing our R D efforts and investments to continue building on our key technology pillars to ensure that we stay ahead of the curve when it comes to test and handling solutions. We prepare our customers to test increasingly complex devices manufactured on the latest process node. SEMI: During your career you’ve driven projects in test and automation and more recently robotics solutions for ports, logistics warehouses and transport. With robotics and automation a key part of Industry 4.0, what role do AEM solutions play in powering the smart manufacturing movement? Nair: The smart manufacturing movement is powered by semiconductors, software and increasingly by artificial intelligence (AI). Test is at the heart of the process of ensuring that semiconductor and electronics devices reach the consumer well-tested for reliability. With our vision of enabling A Zero Failure World, AEM addresses the necessity for safe, highly reliable devices. The semiconductor companies themselves are adopting smart manufacturing methods. AEM’s tools are Industry 4.0-ready, and we continue to invest in machine learning and data analytics, which are integral to the future of test. Our tools are automated and feature embedded sensors to provide our customers with data about tool usage, the state of a machine’s health, and more. Our tools are connected to our customers’ manufacturing automation platforms. Additionally, we continue to invest in our ability to better slice and dice test data to understand trends and patterns to help our customers analyze data and make decisions faster. SEMI: You also have experience heading autonomous vehicle projects. With the COVID-19 pandemic hastening digital transformation, do you see an acceleration in the development of fully autonomous vehicles and smart manufacturing? Research and development efforts for autonomous vehicles (AV) continue at a fast pace worldwide. With shutdowns and restricted movement rules globally, the pandemic has hastened digital transformation in many ways. The delivery of goods and services is transforming, and AV will surely play a part, especially in secure environments for autonomous transport. The pandemic has accelerated the development of autonomous vehicles and smart manufacturing technology in automation-friendly environments like factories and ports. SEMI: At the recent Global Technology Summit hosted by SEMI, you spoke about testing innovations to meet the demands of highly complex devices. Please elaborate on innovative testing solutions versus traditional testing? Nair: AEM offers a disruptive and differentiated solution, one that is driving a paradigm shift to asynchronous, modular, highly parallel, smart testing solutions. ​ The traditional approach of ATEs to test increasingly complex devices on advanced nodes has reached a point of diminishing returns as it gets exponentially more expensive to increase test coverage to acceptable levels. Additionally, as devices get more complex and companies are rapidly adopting heterogeneous packaging technologies, the realization that System Level Test (SLT) is necessary is forcing a rethink of the entire test process. AEM’s provides asynchronous, modular, highly parallel test cell solutions that enable each test cell to run SLT, final test, or burn-in all in one system and its ability to handle hundreds of test cells independently with each test cell testing multiple devices. Our solutions suddenly make comprehensive testing of every complex device cost-effective. Freeing us from legacy ATE allows AEM to provide these innovative solutions to our customers. AEM engineering and manufacturing teams in Singapore at work on semiconductor test and handling systems for global deployment at world-class semiconductor facilities. (Photo credit: AEM) SEMI: Singapore seems to be in the sweet spot of digital transformation. Singapore’s industrial production grew 8.6% year-over-year in January 2021, an expansion driven mainly by a surge in sectors including electronics, and more growth is seen in the year ahead. Digital technologies such as 5G technology and cloud computing together with continued demand for work-from-home equipment is behind this growth. What are the growth prospects for the region’s electronics sector? Nair: Singapore is well-poised to benefit from the current digital transformation accelerated by the adoption of these technologies during the pandemic. Being a safe, well-governed country with strong IP protection, excellent infrastructure, and the rule of law, Singapore is in a great position to play a central role in cloud-based services, 5G, and the semiconductor industry. Singapore’s semiconductor sector output is at a record high, and the prospects for renewed growth in the region are very good. SEMI: As a new Regional Advisory Board member of SEMI Southeast Asia, how is your industry experience relevant to the scope of this role? What opportunities lie ahead for the region? Nair: I am honored to represent AEM in the SEMI’s Southeast Asia RAB. The SEMI RAB can influence policymakers with ideas and information on the current and future needs of the industry. I also believe that SEMI Southeast Asia can cultivate a strong innovative semiconductor ecosystem that helps regional and global growth. I look forward to working with other very experienced and accomplished board members. Bee Bee Ng is president of SEMI Southeast Asia.

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.

As we pass the work-from-home one-year mark, most of us still work remotely and will do so for the foreseeable future. As live trade shows and technical conferences were cancelled one after the other, virtual events became the norm. And, teleconferencing became a way of life. While possibly overstating our role, we have the semiconductor industry – from system design through manufacturing and system integration – to thank for a long history of achievement that made the transition to working remotely relatively seamless and straightforward. The shift, in some cases, took some time to sort out as we set up a workable home office, moved to video conferencing with intermittent connections and settled into a routine. Nonetheless, many of us became more productive and, in some cases, even too productive. Each spoke in the global electronic products hub contributed through creativity and innovation with a pinch of ingenuity and grit. Of course, we could have worked remotely 10 years ago, but not nearly as efficiently. Over the last 10 years, the economy moved to the cloud, producing new opportunities across the global market. Many of these opportunities were made possible by the electronic system supply chain and combination of semiconductor technology, electronic product innovation and people who figured how to leverage it with software platforms to tie it together. Zoom, one of our teleconferencing lifelines, is a good example, as are Netflix, our ongoing source of entertainment, and Roblox, a platform to build games. Facebook, Twitter, LinkedIn and the like sourced the news for us and kept us in touch. Amazon delivered our online purchases and GrubHub brought us our takeout dinners. All rely on cloud computing with thanks to the semiconductor industry. Another great example are data centers powered by semiconductors and the amount of data they processed last year. According to International Data Corporation (IDC), 64.2 zettabyte (ZB) of data was created or replicated due to the dramatic increase in the number of people working, learning and entertaining themselves from home. (Its revised model for global data creation and replication predicts the CAGR will grow to 23% over the 2020-2025 forecast period, a sure bet that the semiconductor industry will address ways to manage the growth, possibly through new AI chips.) Our connectivity is driven by smartphones optimized for low power and the performance of more complex chips. Over the last 10 years, design tools have been enhanced and new methodologies have been introduced to respond to the needs of the increasing complex chips for applications that demand high bandwidth, low latency and reduced power consumption and area. Manufacturing is retooling for higher automation under smart manufacturing initiatives and packaging is even more sophisticated with increasing integration and the 2.5D and 3D packaging rollouts. Let’s take stock of our success. The semiconductor industry has a storied tradition of breakthrough technology since its inception. The consumer electronic product craze started when the first PCs were rolled out in 1971, notes the Computer History Museum. Primitive laptops that followed in 1986 gave way to notebooks in 2007 and the ubiquitous smartphone in 2002 – and the rocket fuel for much of this was the buildout of computer networks, hyperscale datacenters and the cloud. Nothing’s been the same since. The next time we turn on our laptop, click on the link for the latest teleconference from our remote home office in comfortable sweats sitting in our ergonomic chair, let’s take a minute to acknowledge our industry’s grand achievement. And, thank one and all for their contribution and consider what’s coming next. About the Author Robert (Bob) Smith is Executive Director of the ESD Alliance, a SEMI Strategic Association Partner. He is responsible for the management and operations of the ESD Alliance, an international association of companies providing goods and services throughout the semiconductor design ecosystem.

While Artificial Intelligence (AI) emerged in the 1950s, only in recent years have AI applications proliferated with the explosion of data and continuing improvements in Moore’s law that have driven rising processing speeds. Voice assistants, image analysis software, search engines, and speech and facial recognition systems were among the first applications to use AI. Today, adoption has spread to sectors such as agriculture, cybersecurity, healthcare, software development, e-government and the intelligent enterprise to generate jobs and help spur economic growth. The Edge AI Opportunity and the Microelectronics IndustryAI can be embedded in hardware devices such as advanced robots, autonomous cars, drones or Internet of Things (IoT) applications. Today, according to the EU’s digital strategy, data centres and other centralized computing facilities account for the vast majority – 80% – of AI data processing and analysis, with smart connected objects such as automobiles, home appliances and manufacturing robots that bring the compute function closer to the user representing 20%. The latter, known as Edge AI applications, are powered by edge-based machine learning chipsets, not the AI chipsets designed to run cloud-based machine learning algorithms.The EU’s white paper on AI published in February 2020 anticipates that the way data are stored and processed for AI applications will change significantly over the coming five years as edge computing applications proliferate. Most AI applications need to connect with devices that collect data and manage data flows. When the applications connect with cloud infrastructures to train large volumes of data for a machine learning model, the interface devices often require hardware support. Edge AI can minimize data transport by processing data directly from local devices to accelerate data analysis and decision-making and make data transport or accelerator hardware unnecessary, critical in reducing power consumption and enhancing data security for applications such as autonomous driving. Over the past 40 years, the ICT sector has been continuously increasing greenhouse gas (GHG) emissions despite efforts to shift to renewable energy. Cloud-based AI applications require an ICT infrastructure for high-performance computing and high-speed connectivity. According to MIT Technology Review, data centres’ AI workloads could account for a tenth of the world’s electricity usage by 2025. a mass update of cloud-based AI applications may significantly increase energy consumption, unlike with Edge AI. This is why the strategy for developing Edge AI is well-aligned with the EU’s Green Deal objectives. Europe aspires to play a leadership role in Edge AI to strengthen the sector’s competitiveness and protect the European digital sovereignty. Europe’s strong industrial competencies in embedded systems and microcontrollers will help the region promote development of European domestic AI solutions for emerging high-value IoT applications in industrial processes such as Industry 4.0, Connected and Automated driving (CSA), smart cities, climate action, healthcare, and national defence and security. With this strong strategic position in technology, Europe is well-positioned to invest to become the leader in the Edge AI global market.Preparing the Workforce for the Microelectronics IndustryTo design and manufacture leading Edge AI chipsets, European education providers and industry will need to work closely together to train the current and future workforces. Within the framework of the METIS project, a four-year project co-funded by the European Commission through the Erasmus+ programme, SEMI and imec deployed experts in the field to survey and interview focus groups. The survey identified the following key focus areas for workforce development: 1. True Capability of AI and Data Science With AI’s heavy dependence on data, the workforce of the future must be trained in areas of data science including data integrity to ensure quality, unbiased sourcing, collection and accurate analysis necessary to interpret huge volumes of data. Europe also needs to train the next generation of AI chip designers in data security and privacy – key challenges to the widespread deployment of Edge AI chips. 2. Climate Change, Sustainable Development Goals (SDGs) and Social Inclusion TrainingSince the industry must be able to develop Edge AI solutions to enable the digital transformation while limiting GHG emissions, microelectronics engineers need to be schooled in climate change and understand how their work contributes to meeting the United Nation’s Sustainable Development Goals (SDGs). Workplace diversity and social inclusion are also important target areas for education since Edge AI applications should serve various groups of people with different needs.3. EthicsChip industry workers must also be educated in ethical issues of AI related to the technology’s potential societal impact in the near future[1]. With AI applications capable of monitoring Internet searches based on users’ personal preferences and biases to deliver tailored advertising, news and other information, developers must recognize how the technology can influence thinking and behaviour of individuals and groups. This awareness can help developers strike a balance between supporting commercial interests and societal good so the microelectronics industry can ensure ethical implementation of AI. 4. Cross-disciplinary Skills Required for AIAI development requires a comprehensive, cross-disciplinary skill-set to be able to integrate the work of specialists from diverse educational, cultural and professional backgrounds critical to developing non-biased AI solutions. For example, in addition to technical expertise, microelectronics AI developers must be able to communicate clearly and work in close-knit teams with non-technical experts from business, law, medicine and the social sciences.What’s Next?The microelectronics industry has a tremendous opportunity to develop new chip-based solutions for AI architectures, and apply AI techniques to improve operational efficiencies of design and manufacturing. To seize this opportunity, the industry must work closely with education providers to groom the next generation of skilled workers. This tight collaboration is critical to designing and delivering specialised courses to college and university students as well as engineers now working in the chip sector. The stakes are high. By preparing workers to develop Edge AI chipsets, the microelectronics industry can help the world confront some of the greatest challenges it faces today.For more information, see SEMI Responds to European Commission White Paper on Artificial Intelligence.METIS is a Sector Skills Alliance project co-funded by the European Commission’s Erasmus+ Program and coordinated by SEMI. The four year project, launched in November 2019, will develop a Microelectronics Skills Strategy. Based on the strategy, the METIS project will design 43 training modules for 1,100 hours learning in four key areas of the microelectronics sector.We thank Patrick Blouet (STMicroelectronics) and Jeroen Geusens (imec) for their valuable contributions to this article.[1] Ethics of Artificial Intelligence and Robotics, Stanford Encyclopedia of PhilosophyDr. Yanying Li is senior manager of Collaborative Projects at SEMI Europe.Dr. Pushkar P. Apte is the strategic technology advisor for the Smart Data AI Initiative at SEMI

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.