cloud computing

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.

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.

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.

For nearly two decades, Sean Ding, CTO and chief scientist of Alibaba Cloud IoT, has worked in software and algorithm architectures, sensing, semiconductors, systems and cloud computing – all areas that have contributed to the rise of the Internet of Things (IoT). It’s no surprise, then, that Alibaba is leading next-generation innovation for the IoT. Ding will bring his expertise to his role as moderator of Brave New World - MSIG Conference on AI+IoT 2019, a half-day forum March 20, 2019, at SEMICON China in Shanghai, China. Maria Vetrano of SEMI spoke with Ding about technologies key to the IoT era including MEMS, sensors, artificial intelligence (AI), edge gateways and cloud computing. SEMI: MEMS sensors are widely used in IoT devices. What is the relationship between AI and MEMS sensors?DING: While MEMS sensors and AI will increasingly co-reside in end-user devices, I do not recommend adding AI next to the sensor (in the same package). That’s because designers continue to use the ASIC for signal conditioning, so A/D converters are still required. Rather, we should look to edge gateways to carry the majority of the workload, including deep learning, because this reduces system complexity and power consumption.SEMI: Why are smarter sensors shifting data processing and analytics to the edge of IoT devices?DING: Data processing and analytics are very important for IoT devices, but we need to focus on understanding the data, parameter calibration and more. The MEMS sensor industry should leave big data analytics to edge computing and cloud computing because AI requires deep learning, demanding a huge amount of data.The challenge is to find the sweet spot for data processing right next to the sensor element.SEMI: What is China’s evolving role in innovation in MEMS sensors for IoT devices?DING: At present, the MEMS community in China needs to figure out how to innovate instead of copying existing technologies, a low-margin business that will not help to grow the industry. One reason why I am so pleased to see the MSIG Conference on AI+IoT in China is that it will encourage greater creativity in the MEMS community in China, and this will ultimately lead to Chinese companies and R D institutions leading innovation rather than copying it.SEMI: What is the right approach to combining smart MEMS sensors with AI in IoT devices? Why is this important for both domestic Chinese and international markets?DING: Combining data from sensors with cloud-edge computing is the right approach. As sensor companies increasingly provide end-to-end solutions, such as “sensor+ firmware + SaaS + app,” we will realize easier and faster integration of sensors in IoT applications.This is incredibly important because China today is the world’s biggest market for IoT hardware. China has 2,000-plus design houses, 200-plus OEMs and thousands of distributors. That said, we still see a highly fragmented market that will benefit from a faster integration methodology.Faster integration of MEMS sensors and AI/machine learning for IoT hardware will benefit designers in international markets as well.SEMI: What do you hope MISG Conference on AI+IoT attendees will take away from the forum? DING: MEMS sensors are highly fragmented, reflecting the highly fragmented applications in which they play. The MEMS sensors industry should figure out how to provide one-stop-shopping solutions for vertical markets. This will speed the scalability of applications and expedite the growth of sensor production. Sean Ding (柯镇) will moderate Brave New World - MSIG Conference on AI+IoT 2019 at SEMICON China on Wednesday, March 20, 2019, at Kerry Hotel Pudong in Shanghai, China.This conference has been organized by the MEMS Sensors Industry Group (MSIG). Register today to connect with Sean Ding and featured speakers at the event.Speakers at the MSIG Conference on AI+IoT 2019 at SEMICON China include: Welcome and Introduction / 欢迎辞Carmelo Sansone, Director, MEMS Sensors Industry Group (MSIG), a SEMI technology community AI Needs Accurate Data – MEMS Sensors Can Provide It / MEMS传感器为人工智能提供真实数据Andrea Onetti, Group VP of Analog MEMS Group, GM of MEMS Sensor Division, STMicroelectronics Enhanced IoT Edge by Smart Sensors / 智能传感器助力IoT边缘智Bennini Fouad, Regional President Asia Pacific, Bosch Sensortec Horizon AI Processor Solution, Enable Industries in AI Time / 地平线AI芯片解决方案，赋能千万业Carl Zhang 张永谦, General Manager/VP, Smart Chip Solutions Division, Horizon Robotics Inertial Sensors in AI Applications / 运动传感器AI应用案例Ben Lee 李彬 , CEO, mCube Ultra-Low-Power Solutions: an Ecosystem Approach / 超低功耗的生态链解决方案Carlos Mazure, IEEE Fellow, Chairman Executive Director, SOI Industry Consortium High-Integrity, Fault-Tolerant Open Inertial Measurement Platform for AI-based Vehicle Automation / 适用于人工智能车辆自动控制的高集成及容错的惯性测量开放平台Dan Dempsey, Senior Director of Automotive, ACEINNA Maria Vetrano is a public relations consultant at SEMI.

I really don’t know clouds at all. – Joni MitchellThe semiconductor industry is finally on the cusp of joining the cloud revolution. The cloud has offered the promise of greatly expanded resources for years, but adoption has been slow due to lingering concerns. The biggest contributing factor for the concern over moving from on-premise EDA servers to cloud-based servers is, surprisingly, the rise of third-party IP. In the old days, if you were developing 100 percent of your own IP, and if you put that IP on a public cloud, and it somehow leaked out, well shame on you. That would certainly be bad for business. It might hurt your reputation a bit. But these days, with so much third-party IP being embedded into chips, if that third-party IP leaks out, that’s a lawsuit-fest in the making.Consequently, semiconductor companies now have even more incentive to protect IP with advanced security. Surprisingly, cloud-based security is far, far better than on-premise security. Why? Because keeping customers’ data secure is the central mission of cloud service suppliers, so they’ve developed a rich set of security tools to protect the data that’s entrusted to them by their clients. In many ways, you can maintain much better security in the cloud than you can with on-premise tools. Image credit: Markus Spiske temporausch.com from Pexels Amazon Web Services: Exemplifying the benefits of cloud computingTake Amazon Web Services (AWS) as an example. (Note: AWS is not the only vendor in the cloud space, but it’s one I’m very familiar with.)AWS has developed the concept of security groups – firewalls that you throw up around any network interface to allow only specific traffic into that secured network. You can do that for just one server or for a fleet of servers, in just seconds. Most on-premise server networks won’t let you work that quickly, or as easily, or with such fine control because most such networks lack the security tools to do this.In addition, AWS allows you to encrypt every bit of data stored on and flowing through its cloud-based storage systems. You can encrypt data at rest in on-premise storage but it’s a lot harder to encrypt data flying through the on-premise network. Amazon’s Elastic File System (EFS), a managed NFS file service, offers the ability to easily encrypt NFS traffic on the wire, a difficult feat at best with an on-premise solution.AWS built-in encryption key-management service can rotate encryption keys automatically. The cloud also allows you to have key policies that are easy to implement and maintain.Internal corporate networks rely heavily on perimeter firewalls for security. Perimeter defense just cannot deliver sufficient security against determined hackers and everyone realizes this. We’ve built big, open, on-premise networks that are just not well-suited to implementing adequate security protocols. Trying to retrofit these network architectures with additional security is time-consuming and costly, and it hurts engineering productivity. Moving to the cloud gives you a greenfield opportunity to right some of the wrongs of the past.Continuing with AWS as an example, here are some additional advantages of EDA in the cloud: AWS provides physical security that’s far above and beyond on-premise security. It doesn’t publish the physical locations of its data centers. It also has professional security staff 24/7, keycard access, and additional security features that far exceed typical on-premise physical security. AWS automatically manages security patches and access controls for their managed services such as database services. AWS gives you plenty of security tools to automate security processes, audits, and so forth to protect your data. AWS gives you so much flexibility that you can get yourself in trouble in you are not careful. If you want, you can create the same sorts of security holes that already exist with on-premise networks. You shouldn’t of course, but you can if you’re not thoughtful about things. You just need to hire the right people to implement and maintain your cloud security.Here are five very big differences between AWS (cloud-based) and on-premise server networking: Elasticity: Cloud-based systems enable you to scale up in minutes. That ability has pluses and minuses depending on how disciplined you are. On the plus side, you can quickly grow your EDA infrastructure as big as you want and then shrink it back down when you no longer need the additional capacity. All you need to do is tell the cloud service that you need more capacity and it will bring that extra capacity online for you in minutes – and will charge you for it. (That’s the minus side.) When you’re done, you can turn off the extra capacity (and stop paying for it) with the same speed. If you want to provision more EDA capacity for your on-premise network, you’ll need to beg, borrow, or steal existing capacity from someone else on your network, or you can order more servers, get the vendor to build and ship them, install them in your server room, provision them, and bring them online. That will take months. Fault tolerance: On-premise networks rely on large, monolithic service architectures, which saddle EDA vendors with more than 30 years of technical debt. The cloud operates on a different model, one that’s based on containers and microservices. This is inherently a redundant, fault-tolerant computing model if you write your code correctly. The difference between redundancy in the cloud and in on-premise networks is night and day. There’s no comparison. No private networks can match the available and growing redundancy of cloud systems, which have redundant servers inside of a data center and redundant data centers in multiple, worldwide geographic locations, which protects your data from natural and man-made disasters. Network segmentation: Many semiconductor developers have several design centers distributed around the world and there may be IP in use on a project that cannot be shared with certain geographic locations either by law or by contract. Cloud networks are already set up with automated tools for network segmentation that can enforce geography-specific rules through VPCs (Virtual Private Clouds), which are easy to set up. VPCs allow you to set up subnets with restrictions based on routing tables so that IP management and control become highly automated. Removal of single points of failure: The typical EDA grid configuration has several built-in single points of failure. For example, a central job dispatcher generally runs on one single node. If that node dies, all EDA work halts. The same is true for EDA license servers and for configuration-management and version-control servers. Again, because cloud networks are based on the microservices concept, the cloud simply doesn’t need to have the same single-point-of-failure vulnerabilities that on-premise networks have. On-premise networksTo get these same advantages with on-premise networks, the grid architecture must fundamentally be changed, starting with the replacement of NFS. EDA systems need to replace huge, monolithic file systems specifically developed for EDA with object storage. That's a tall order – one that requires the rewriting of fundamental assumptions that serve as EDA software’s foundation.In the 1980s, 1990s, and early 2000s, small EDA startups appeared to fill gaps in the offerings of the large EDA players. If they succeeded and grew, they’d eventually be gobbled up by a larger EDA vendor. That flowering of EDA startups seems to have damped down. The market has really matured.Next wave of EDA startups to offer cloud-first toolsGoing forward, I expect the next wave of EDA startups will be offering cloud-first tools that are not burdened by three decades of technical debt. They’ll be able to architect their tools specifically for the cloud.We’re starting to see this happen. For example, Metrics, a Canadian EDA startup, offers a pay-by-the-minute, cloud-based simulator and verification manager. Although one job on one cloud server might run slower than a monolithic simulator running an on-premise server, Metrics has architected its tools so that you can throw more servers at the problem, allowing you to run all of your jobs at once. Here, multiple simulation jobs running concurrently on multiple servers will ultimately finish faster than running the jobs serially on one slightly faster on-premise simulator.That’s the kind of innovation that we’re going to see. That’s the future of EDA.Derek Magill is executive director and president at HPC Pros. Derek has 20 years of experience supporting semiconductor engineering functions. His main focus has been in system architecture and technical management, but over the years he has been involved with technologies such as EDA licensing, ClearCase, HPC architecture, IP management and engineering software support. Derek spent 15 years at Texas Instruments in various technical and managerial roles. He is currently a senior manager, IT at Qualcomm managing the Global License Infrastructure team as well as the lead technical architect for the company's engineering cloud activities. The Electronic System Design (ESD) Alliance, a SEMI Strategic Association Partner, is the central voice to communicate and promote the value of the semiconductor design ecosystem as a vital component of the global electronics industry. As an international association of companies providing goods and services throughout the semiconductor design ecosystem, it provides a forum to address technical, marketing, economic and legislative issues affecting the entire industry. The ESD Alliance also stages events that promote networking, learning and collaboration among member companies. To learn more about the ESD Alliance and how to join the group, visit www.esd-alliance.org or contact Bob Smith at [email protected].