Industrial IoT

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.

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

The world’s most advanced manufacturing factories are leading the way in driving efficiency and sustainability.In advance of its 2020 meeting, the World Economic Forum welcomed Micron into its Global Lighthouse Network, a group of advanced manufacturers “that are showing leadership in applying the technologies of the Fourth Industrial Revolution to drive operational and environmental impact.”For years, Micron has been helping clients integrate artificial intelligence (AI), big data analytics and the industrial internet of things (IIoT) into their factories. And now Micron’s factory is one of the first facilities in Singapore, along with Infineon, to be recognized by the Global Lighthouse Network.In a recent interview with Channel News Asia, Manish Bhatia, executive VP of Global Operations, explained how Micron has been practicing what it preaches: “Our products enable new technology trends such as IoT, 5G, cloud computing and autonomous driving. Applying these technologies in our own manufacturing facilities demonstrates the enormous potential in driving business value. Industrial IoT and artificial intelligence are part of the biggest revolution since the advent of robotic manufacturing productivity 50 years ago.”For Micron, this journey started with the need to “keep pace with the technological advancement of our semiconductor processes,” Manish said. “We wanted to provide higher-capacity, higher-performance, lower-cost and lower-power chips.”This meant embarking on the same journey they guide clients through: “We started by focusing in 2014 on simple statistical analysis to improve our production processes,” Manish said. “Following that, we developed more complex deep learning and AI capabilities to draw insights from our data. Most recently, we introduced IoT sensors — like cameras and acoustic sensors — to gather even more data that allows us to further improve our production processes.”The Singapore factory plays a critical role in developing leading-edge NAND. Micron’s Singapore presence, composed of two wafer-fabrication facilities and one assembly and test facility, serves as the base for worldwide operations. With over 500,000 square feet of cleanroom space, the location is also a designated NAND Center of Excellence, driving the implementation of the company’s leading-edge 3D NAND production for use in mobile phones, solid-state drives, digital cameras and more. Micron employs approximately 8,000 people in Singapore.The World Economic Forum says the results of the Singapore transformation have been spectacular: Micron’s “semiconductor fabrication facility has integrated big data infrastructure and IIoT to implement artificial intelligence and data science solutions, raising product quality standards and doubling the speed at which new products are ramped.”Below are notable achievements that Micron was recognized for: Automation of production and maintenance produced a 4% tool availability improvement. The IIoT-enabled smart factory led to a 22% scrap and product downgrade reduction. Advanced analytics for process optimization with OEMs reduced time to ramp new products by 50%. Deep learning optical-defect detection created a 2% yield improvement. The integrated deviation management platform reduced time to resolve quality issues by 50%. Micron was a natural choice for the Global Lighthouse Network, an organization whose creation is timely. The World Economic Forum points out that “global production industry is lagging in its adoption of Fourth Industrial Revolution manufacturing technologies, with more than 70% of companies stuck in pilot-phases … [There is] a need for a neutral learning platform to showcase top-use cases, roadmaps and organizational approaches to adopting and scaling technologies from which other companies globally could benefit.”As part of the Global Lighthouse Network, Micron will be able to share knowledge and best practices with peers, support new partnerships and help other manufacturers deploy technology, adopt sustainable practices and transform their workforces. We can all build on this community of like-minded organizations, levering technology to improve efficiencies and promote sustainability.This recognition from the World Economic Forum is a win-win. We look forward to joining the club of lighthouse factories around the world and to helping propel the entire global manufacturing industry into the Fourth Industrial Revolution. At Micron, we are at the forefront of this transformation and welcome the opportunity to serve as a lighthouse.Koen De Backer is responsible for driving Micron’s smart manufacturing initiatives and digital operations including capabilities with IoT, artificial intelligence, advanced analytics, cognitive computing and machine learning to enhance Micron’s business, global operations and product development. Prior to joining Micron, Mr. De Backer led large-scale operations projects for more than a decade to help clients reduce inefficiencies and achieve excellence in manufacturing, procurement, supply chain and support functions.Most recently, De Backer was a partner at McKinsey Company, where he steered the semiconductor consulting practice in Southeast Asia and was one of the firm’s leading experts on applying artificial intelligence and automation techniques across operations and support functions such as finance, human resources and procurement. Additionally, Mr. De Backer consulted with high-tech global clients while working at Deloitte Consulting, Altran Europe and CSC. Mr. De Backer holds a master’s degree in business administration from INSEAD and a master’s degrees in both industrial management and electromechanical engineering from Katholieke Universiteit Leuven.De Backer is also chairman of the SEMI Southeast Asia Smart Manufacturing Chapter. For information on participating in the chapter, contact Shannen Koh at [email protected].

The microelectronics industry is entering the era of Cloud Engineering Simulation to slash the costs and risks of new technology development and speed time-to-market in spaces like semiconductors, MEMS sensors, RF front ends, biomedical and driverless cars. In the run-up to SEMICON Europa, 12-15 November, 2019, in Munich, Germany, SEMI spoke with Ian Campbell, CEO of OnScale, about the new paradigm of Cloud Engineering Simulation. Campbell shared his views ahead of the SMART Design Forum, 14 November, 2019, 14:30 to 17:00, in Hall B1, TechARENA 1 at SEMICON Europa. Registration is open. Join the forum to meet experts from OnScale and other key industry influencers. Attendance is free of charge for all SEMICON Europa visitors.SEMI: How did your adventure with OnScale start?Campbell: I’m an engineer. When I was still in high school, I took a night class at Nashville Tech to learn AutoCAD R14, and I’ve been designing and engineering things ever since. I was introduced to Desktop Simulation in my bachelors of mechanical engineering program and used many types of simulation tools for massive design studies at the Aerospace Systems Design Lab at Georgia Tech. I’m a simulation junkie.I started my first Silicon Valley high-tech company, NextInput, in 2012 with Dr. Ryan Diestelhorst (now VP of Strategy at OnScale), to commercialize new ForceTouch and 3D Touch technologies based on our patented MEMS force sensors. At NextInput, we bought hundreds of thousands of dollars of engineering software, but were always frustrated by slow, inaccurate engineering simulation results. We dreamed about running massive simulations on Cloud Supercomputers and creating true Digital Prototypes that could replace costly, time-consuming, and risky physical prototypes.When I got the chance to join the team that became OnScale in 2017, I jumped at the opportunity. At OnScale, we took engineering simulation solvers that had been developed for the U.S. military to run on U.S. Department of Defense and DARPA supercomputers and built a cloud supercomputer platform on Amazon Web Services to run the solvers. The net-net is the world’s first on-demand, infinitely scalable Cloud Engineering Simulation platform. Now, we routinely run massive multi-billion degree of freedom simulations for Fortune 100 companies, including many from the semiconductor and MEMS industries. Since our business model is to charge per core-hour for simulations, the incredible capability we built is cost-effective and available to small startups as well. SEMI: How is the semiconductor design ecosystem evolving? How is Cloud Engineering Simulation applied to semiconductor and design industries?Campbell: The entire industry is experiencing a massive acceleration in product launch cycles and increased competition. New markets like IoT and 5G are reducing semi/MEMS product cycles from years to months. That, in turn, puts enormous pressure on semiconductor and MEMS designers. Missing a key product introduction like a flagship smartphone launch can literally make or break a company.A reliance on traditional engineering methods – schematic capture and layout of a chip, taping out (physically prototyping the chip), performing engineering validation on an e-bench, qualifying the chip (or not qualifying it and going back to the drawing board), and finally launching mass production – is no longer sustainable from a competitive perspective.Instead, market-leading firms are turning to Cloud Engineering Simulation and Digital Prototypes to explore massive design spaces, find optimum designs that beat the competition in every KPI (size, power, performance), and digitally qualify designs before ever cutting silicon, ensuring that designs are robust over their intended operating environments and performance envelopes. Large thermal analysis of a chip on a circuit board executed quickly on the OnScale Cloud Simulation Platform SEMI: Can you give us an example? Campbell: A great example is thermal analysis. Thermal effects have always had huge impacts on MEMS device performance and, more recently, they are beginning to impact performance of next-gen semiconductors, especially GaN power electronics for electric vehicles (EVs).Conducting a full system-level thermal analysis of something like an EV power management system – a power IC in a package, on a board, in an enclosure, under various loading conditions – has been a challenge from a simulation complexity perspective (degrees of freedom) and from a parametric sweep perspective (running hundreds or thousands of simulations to optimize chip placement, routing, etc.). To run these sets of simulations using legacy desktop simulation would take weeks, perhaps even a month or more. To run these massive simulations in parallel on cloud supercomputers using OnScale takes days or even hours.Our customers routinely run very large simulation studies on OnScale Cloud for thermal simulations, RF filter simulations, MEMS simulations, packaging simulations (what we call Digital Qualification), and many more use cases.SEMI: What’s one of your strategic objectives for 2020? Campbell: For 2020, we’re doubling down on MEMS and semi simulation capabilities. We will be launching additional solver capabilities like EM that will be critical in our strategic markets like 5G. We will also be launching a Cloud API so that engineers can integrate OnScale directly into their existing engineering workflows (e.g. MATLAB or EDA/CAD tools) with just a few Python commands.SEMI: Can you share one prediction for the future of semiconductor design solutions? share?Campbell: I think we will continue to see MEMS and semi designers push the envelope and bring smaller, more performant, more cost-effective solutions to market. I’d like to see more highly cost-effective flexible semi/MEMS designs come to market to enable next-gen IoT and IIoT applications. I’d also like to see more biomedical applications – biomems, microfluidics, and labs on a chip for all sorts of life-enhancing applications.SEMI: What are your expectations regarding the SMART Design Forum at SEMICON Europa 2019 in Munich? Campbell: I’m looking forward to getting back to my roots in MEMS/semi design and chatting with other designers about the future of engineering and the future of semi! Ian Campbell is a twice venture-backed Silicon Valley CEO and expert in MEMS sensors, semiconductor technology, and engineering software. Most recently, Ian co-founded OnScale, a Cloud Engineering Simulation startup backed by Intel Capital and Google’s Gradient Ventures. OnScale is revolutionizing engineering by combining world-class multiphysics solvers with Cloud supercomputers, machine learning, and artificial intelligence. Prior to co-founding OnScale, Campbell served as founder and CEO of NextInput, where he led the startup through multiple rounds of funding – totaling $12 million and an additional $4 million in research contracts with government and industry partners – and built a world-class team of engineers and scientists who developed 3D Touch and ForceTouch technologies for smartphones, wearables, industrial, and automotive interface applications. He also secured the first major smartphone OEM design wins in Asia. Campbell earned his B.S. in mechanical engineering from Middle Tennessee State University, and his MSAE in aerospace engineering and MBA from Georgia Institute of Technology.Serena Brischetto is senior manager, marketing and communications, at SEMI Europe.

Artificial intelligence (AI) is on the verge of transforming entire industries as it gears up to power semiconductor industry innovation and growth, thrusting the technology to front and center at SEMICON Japan 2019, December 12-14 at the Tokyo Big Sight (Tokyo International Exhibition Center).The SMART Technology Forum at SEMICON Japan will highlight the latest AI developments and trends. Supported by U.S. Commercial Service in Japan, the forum will feature Yutaka Matsuo of the University of Tokyo. An authority on AI, Matuso will give an overview of both AI business and technology. His presentation will be followed by an AI outlook from Microsoft Japan, Amazon Web Services and DefinedCrowd.A number of Japanese startups are on leading edge of AI innovation in machine and deep learning. One is Preferred Networks Inc., a company that applies cutting-edge deep learning technology to Internet of Things (IoT) applications across transportation, manufacturing and healthcare.In his opening day keynote at SEMICON Japan, Toru Nishikawa, president and CEO of Preferred Networks, Inc., will highlight the latest developments and promise of using deep learning for industrial applications. Nishikawa will unpack how AI companies jockeying for competitive advantage will win by harnessing technologies to process massive amounts of data efficiently and quickly.Following is look at Preferred Networks, Inc. and five other Japanese startups that are driving AI innovation. Within Japan's world of AI, machine learning, and deep dearning, Preferred Networks is likely the most well-known Japanese company. The parent company, Preferred Infrastructure, was founded in March 2006 by Toru Nishikawa and Daisuke Okanohara, who focused on search engine development before turning to machine learning and establishing Preferred Networks to commercialize the technology.Preferred Networks established itself as one of the world’s top providers of machine learning technology with the development of Chainer – an open source deep learning framework that has been offered free of charge since June 2015 and was released before TensorFlow, Google’s renowned Deep Learning framework. Established in 2012, ABEJA is thought to be Japan’s first venture company to specialize in deep learning. ABEJA's core technology is its AI platform ABEJA Platform. Based on this platform, the company offers various solutions to more than 100 client companies. ABEJA also offers ABEJA Insight, a specialized package service for the retail and distribution, manufacturing, and infrastructure industries. Data analytics provider BrainPad Inc. was the first Japanese AI venture listed on the Tokyo Stock Exchange. Established in 2004, before the advent of big data, BrainPad Inc. cultivated a vision of analyzing vast amounts of data in increase the competitiveness of Japanese companies. LeapMind Inc. aims to offer deep learning technology that uses fewer computing resources and draws less power. Both are important capabilities since deep learning requires considerable computing resources to perform image and speech recognition. The company’s answer to this deep learning challenge is a small form factor FPGA with low power consumption.In April 2018, LeapMind started offering the tool DeLTA-Lite to support model construction for Deep Learning. The tool simplifies the development of deep learning design models, eliminating the need for model design, hardware, and software expertise. Hacarus Inc.’s HACARUS-X AI technology, which combines sparse modeling and machine learning technology, features low power consumption and small devices such as FPGAs. In collaboration with semiconductor trading company PALTEK, Hacarus is integrating HACARUS-X algorithms with Xilinx's FPGA Zynq UltraScale + MPSoC. Both companies area also implementing HACARUS-X algorithms in a box computer.Sparse modeling is gaining attention as a modeling method by which humans can understand the judgment process of AI by extracting features from a small amount of learning data. With expertise in life science fields such as medical and biology and image processing technology, LPixel, Inc. develops image analysis systems with original algorithms and machine learning techniques. It has developed a cloud-based AI image analysis platform and an AI medical image diagnosis support technology that streamlines the review of large amounts of research data and detects image fraud in research papers and other documents for the medical and biology fields, freeing researchers to devote more time to their core work. Yoichiro Ando is a marketing director at SEMI Japan.

Nicolas Sauvage, senior director of Ecosystem at TDK InvenSense, will present at the fast-approaching MEMS Sensors Executive Congress on October 29-30, 2018 in Napa, Calif. SEMI’s Nishita Rao spoke with Sauvage to offer MSEC attendees advance insights on Sauvage’s feature presentation.SEMI: What is “autonomy value” and why is it important?Sauvage: How do you increase the perceived value of an electronic device? If it’s an autonomous car, its value is closely tied to the autonomy level — i.e., the independence — that it offers people. Higher autonomy value for a self-driving car, for example, means that even a blind person could use it. It’s been almost two years since Waymo demonstrated this, and here’s the video that shows it.Countless other sensor-based electronic products have their own “autonomy value.” Imagine the need to get medicine to people during a humanitarian health crisis. Drones could be your best option because they can deliver to inaccessible or remote locations. Unlike older drones, which require active piloting by a person, a drone with higher autonomy value could deliver medicine to Doctors Without Borders without ongoing human intervention.This drone could navigate objects, such as trees and birds, and would have excellent location-awareness. It could fly through any landscape in bright sunlight or during the night. To increase the drone’s autonomy value, you would need better sensors, including those sensors that can enable sensing in sunny conditions or in pitch-black night, as well as better machine learning.SEMI: In this example, what types of sensors would the drone manufacturer need?Sauvage: The manufacturer would need a “surrounding-sensing” solution that includes ultrasonic and pressure sensors as well as image sensors. Start with high-quality image sensors combined with ultrasonic range-finding sensors — high-accuracy devices that function in all lighting conditions and can detect objects of any color. Add motion sensors and a pressure sensor, which would capture the height of the drone to make known the drone’s location in space. The drone would need this combination of sensors, plus smart sensor fusion, because GPS alone cannot avoid obstacles: its signal can be sporadic in certain parts of the world or in certain terrain, making it unreliable.A key attribute of all these sensors would be low power consumption since the drone would run on battery.SEMI: To what extent might autonomy value cause manufacturers to consider multi-vendor solutions?Sauvage: I would like to see it inspire the MEMS and sensors ecosystem to work together, to arrive at multi-vendor solutions that will benefit humanity through greater autonomy value. Whether we’re looking at autonomous cars, drones, robotics or other applications, there are cases where we need to prioritize safety and security over industry competition. SEMI: Where are we today in terms of achieving true autonomy value – and where are we going?Sauvage: The sky is the limit, literally. Machine learning and surrounding-sensing solutions applied to cars, drones and robots will increase autonomy value to the point where we can justifiably call it artificial intelligence.SEMI: What would you like MEMS Sensors Executive Congress attendees to take away from your presentation?Sauvage: I hope that attendees will recognize the value of ecosystem solutions in increasing autonomy value. Together we can expand the variety of sensor types that address novel use-cases and jobs-to-be-done. Instead of waiting for customers to ask for ecosystem-level solutions, we need to articulate a complete MEMS and sensors supply-chain ecosystem if we want the Internet of Things (IoT) and Industrial IoT (IIoT) to grow more quickly. As senior director of Ecosystem, Nicholas Sauvage is responsible for all strategic relationships, including Google and Qualcomm, and other HW/SW/System companies. He is also responsible for strategic and market-driven goal-setting of our SensorStudio developer program, and driving select partnerships with SoC sensor hub platforms. Prior to joining InvenSense, Nicolas was part of NXP Software management team, responsible for worldwide sales, as well as for P L and product management of their OEM Business Line. Nicolas is an alumnus of Institut supérieur d’électronique et du numérique, London Business School and INSEAD. Register today to connect with Nicolas Sauvage at the event. You can also connect with him on LinkedIn.Nishita Rao is a marketing manager at SEMI.

Over the last three years the number of battery-operated electronic-component solutions for the Internet of Things (IoT) and Industrial IoT (IIoT) applications has been increasing steadily. This trend will continue for years to come, particularly with the growing popularity of mobile devices of all flavors. Addressing power consumption for battery-powered always-on IoT/IIoT devices – which rely on dozens of electronic components, including sensors — is critical to their commercial success.The demand for ultra-low-power sensors has accelerated the race to squeeze every last mW from components. Compared to previous-generations of sensors, semiconductor suppliers have managed to drastically reduce power by as much as 50%-60% over older solutions. Leveraging new state-of-the-art analog design techniques, we have effectively optimized capacitive readings of MEMS structures. How effective are they? We estimate that with the right mix of our company’s power-saving technologies, it is possible our customers could save 3MW/year globally[1].What’s next?While the semiconductor industry continues to investigate novel technologies, approaches and analog IP for greater energy efficiency, we believe that bigger gains in reducing power consumption will come from thinking at the system level. The sensor node is a good place to start.A typical IoT node is composed of a set of sensors, a microcontroller, a radio frequency (RF) link, and a power-supply system, often based on Li-Po batteries.Of these, the microcontroller and RF link consume the most energy and, in the RF link, power consumption is a function of the distance between end point and receiver and of the amount of data transmitted. Thus, at longer distances reducing the amount of data transmitted can save power. We can achieve this by including some pre-elaboration capabilities on-board and by extracting more meaningful information from the raw sensor data.We address this by moving some computation and data analysis inside the sensors, where smart hardware “digital blocks” perform faster and more efficiently than software-based routines running in the microcontroller. We can achieve this by using dedicated hardware resources to reduce overall system power consumption. The beauty of this solution is that it allows the microcontroller to operate in low-power states by only transmitting significant information in batches. The SensorTile development kit can speed up prototyping of ultra-low-power IoT devices by integrating an ultra-low-power MCU and BlueNRG Bluetooth radio with sensors. Some examples of these advanced digital blocks are the Advanced Embedded Pedometer, the Finite State Machine and Decision Tree, and Compressed FIFO in an IMU.The Advanced Embedded Pedometer is a hard-wired step counter that works independently inside the sensor, without CPU intervention: By comparing sensor outputs to pre-defined and -loaded patterns, it autonomously decides whether the user is walking or running to start and stop counting the user’s steps. The sensor then makes this information available to the microprocessor for further elaboration or for simple notification to the user.The Finite State Machine and Decision Tree are new functions dedicated to pattern recognition (machine learning) and decision-making: They can perform complex classifications and state detection, and can send dedicated warning and signaling to the microprocessor. A good real-world example is industrial predictive maintenance, where the sensor can categorize and identify different malfunctioning states in the equipment before waking the microprocessor to react.Our products, on average, save about 1 mA (1e-3) over competitive devices or over our previous-generation parts. So 2.0 x 1e-3 x 1.5e9 = 3MW. Programmable Sensor and Decision Tree Finite State Machine Integrating programmable sensors and decision trees as well as finite state machines in the sensor allows the sensor to do more of the work while the MCU sleeps. Source: STMicroelectronics Another example is compressed FIFO (first-in, first-out) buffer, which can store sensor data in the sensor, not in raw format, by using efficient compression algorithms. In addition to saving memory (and therefore silicon area) inside the sensor chip, it also saves power by reducing the number of bytes transferred to the processor and by shortening the communication data flow, which reduces processor-active time.These examples – the Advanced Embedded Pedometer, the Finite State Machine and Decision Tree, and compressed FIFO buffer – are just some showing that we can develop low-power IoT/IIoT devices through intelligent management of sensors, microcontrollers and other components in any given system. Your starting point is an IoT/IIoT node that lets you selectively allocate some power-hungry tasks — such as computation and data analysis — to sensors instead of the microcontroller. Leveraging data blocks that reside in the sensors alleviates the microcontroller’s typical power drain, allowing the microcontroller to operate with maximum efficiency.[1] ST sells about 1.5 billion pieces/year (1.5e9), which typically run from a 2V supply. Luca Fontanella joined ST Microsystems in 1995 as an analog designer. In 2001 he joined the MEMS team in a marketing role and today he is marketing manager in the MEMS Sensor Division. Luca has contributed to 25+ international patents and has presented at multiple conferences. He earned a degree in Electronic Engineering from Padua University. Simone Ferri joined STMicroelectronics in 1999 as Central R D engineer, moved to the Audio Division as a digital designer and is now director of the Consumer MEMS Business Unit. He holds a degree in Electronic Engineering and an MBA from the Polytechnic of Milan. _______________________________________________________________________________________________Brush up on the latest MEMS and sensors trends and gain a new perspective on emerging applications. Register today!