Infineon

To help build the MEMS and sensors industry’s understanding of the Smart City challenges facing city technical and management personnel (and their integrators), MSIG held an invitation-only Sensorize Your City workshop for sensor device manufacturers and integrators on April 5 in Columbus OH.

SEMICON Europa, SEMICON West, SEMICON Japan, & SEMICON Taiwan welcome back exhibitors and visitors to show floors starting in mid-November as four of SEMI’s seven flagship events gather visionaries and industry leaders to explore the latest semiconductor trends and innovations by the end of 2021.

METIS, a Sector Skills Alliance project co-funded by the European Commission’s Erasmus+ Program and coordinated by SEMI, recently launched an online questionnaire aimed at gauging the skills and expertise the industry needs to drive continued growth over the next five years. The survey, which will stay online until 15 October 2020, is a part of the METIS project’s efforts to involve a broad range of stakeholders in the microelectronics industry to assess workforce, future technology and economic trends influencing talent development and the skills needed most today and in the next five years. The survey aims to highlight the skill mismatches in specific job profiles that are of increasing importance to the microelectronics industry. It elaborates on the upskilling and reskilling needs for design engineers. Given that semiconductor design is becoming increasingly crucial for Europe’s competitiveness and technological sovereignty, the new skills required from design engineers are a priority area for the METIS project. Other examples are the manufacturing and maintenance technicians, two job profiles that are currently experiencing significant shifts in their skillsets, as COVID-19 has thoroughly transformed their way of work.While the microelectronics industry has been very aware of the importance of the high level of investment in R D, it is equally crucial to ensure that the workforce of the industry is equipped with knowledge and skills for the rapid technological developments. Maintaining high levels of investment in workforce including attracting talent, updating their knowledge and skills with the latest technological development, and supporting them to lead innovations, is essential for this industry. There is a growing demand for specific requirements for this sector to support innovation in many other sectors such as automotive, energy, healthcare, and government, to foster benefits from emerging digital technologies such as Cloud Services, Internet of Things (IoT), Artificial Intelligence (AI), Digital Reality, and Blockchain.In addition to the online questionnaire, the METIS project consortium is interviewing top experts from leading microelectronics companies, education representatives from universities and training academies, and experts from government agencies and industry associations. The interview outcomes provide inputs on what kind of employee profiles are the most difficult to find, what skills this sector is looking for in a candidate, and what kind of training and policy frameworks are needed to improve employers’ skills. Those inputs are essential to develop the skill strategy and form recommendations on training modules.Furthermore, the METIS project consortium is organizing 10 focus groups. Each of the focus groups is dedicated to a key topic, such as SC design, SC materials, semiconductor manufacturing equipment, etc. For example, one of the METIS focus groups is dedicated to Edge AI, a top priority for the microelectronics industry. Strengthening the AI talent pipeline is essential to harness the potential of Edge AI in Europe and to facilitate the shift from the Cloud to the Edge when possible in order to meet specific demands (e.g. for autonomous driving), reduce energy consumption for data communications, and to increase efficiency. The EU’s White Paper “Artificial Intelligence - A European approach to excellence and trust”[1] , published this February, also emphasizes the importance of upskilling and reskilling to position Europe among the global leaders in AI. Hence, the focus group will work towards pinpointing the skills necessary for the semiconductor workforce to capture the potential of the trend.The results of the survey, interviews and focus groups will be used to form the Microelectronics Skills Strategy. Based on this strategy, the METIS project will design 43 training modules for 1,100 hours learning in four key areas of the microelectronics sector:Component designSystem designBasic of manufacturingKey competencies and innovative thinkingThe METIS project is planning to recruit 2,000 learners in companies and education and training institutes to participate in the trainings and validate the impact. The METIS project will also work with companies, education and training providers to ensure continuity of the initiative and foster cooperation.During the METIS project course (2019 – 2023), the Skills Strategy will be updated yearly to reflect the latest technology and market trends. To enable the Skills Strategy to continue serving the industry, METIS is working on forming a permanent instrument, named Observatory and Skills Council, to continue developing the skills strategy, update the training and facilitate cooperation between industry and education and training providers.Laith Altimime, president of SEMI Europe, and 50 members of the Microelectronics Training, Industry and Skills (METIS) consortium The METIS consortium invites companies and associations involved in microelectronics training and education provision, human resources and career services professionals, technology strategists and policy makers to complete the online questionnaire. Stakeholders are also welcome to subscribe to the METIS newsletter for the latest on METIS programs. For more details, please contact Yanying Li at [email protected].[1] EU’s White Paper on Artificial Intelligence available at: https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdfDr. Yanying Li is senior manager of Collaborative Projects at SEMI Europe.

What’s next for smarter, more connected electronics manufacturing - Part 3 The fast-maturing infrastructure now enabling analysis of exponentially larger data volumes brings the microelectronics industry to an inflection point, where the winning companies will be the first to master the use of this data to solve the industry’s emerging challenges. SEMI expands its coverage of these vital issues with a Smart Manufacturing Pavilion and three days of talks SEMICON West, July 10-12 in San Francisco. While deep learning is starting to be applied to image recognition for wafer inspection, it is also being considered for sequential pattern recognition in order to evaluate equipment parameter traces. The next emerging applications will start to use those learned patterns to predict outcomes, and then use those predictions to automate process control. One early application of deep learning is IC process development. “People don’t think of research and development as the first place to automate, but it’s where applying our digitization and simulation has first had impact,” says David Fried, Coventor vice president of Computational Products. He noted that insertion is easier in the lab than in the fab. Technology at 10nm and beyond is now so complex that companies at the leading edge must use process modeling to understand the effect of process variation on their designs. Learning cycles can now be accelerated during development by simulating 10,000 digital wafers instead of running 25 actual wafers during screening, Fried says. Applying structured analysis and machine learning to the data simplifies optimization across the 500 or more interrelated process steps. Coventor has recently introduced a statistical analysis package that aids the design and analysis of process variation experiments by using large volumes of data from its models. Fried says these models are next being used to accelerate the yield ramp in manufacturing. Digital simulation also could speed development of high-mix, lower value products While digital twins are best known for their use in complex, high value products like jet engines, the simulation technology could also enable the electronic manufacturing services (EMS) sector to reduce the time, cost and risk of developing its high mix of products. “The EMS sector’s use of digital twins will be vital for it to smooth the move of CAD/CAM digital design data for so many different products into manufacturing, and to accelerate validation testing of designs and products by doing more of it in the virtual world,” says Dan Gamota, vice president of Engineering and Technical Services at Jabil. Gamota also highlights the push for traceability from the automotive and healthcare markets, where the digital models could be used to quickly assure that the design was built exactly as specified. “In the past year, traceability has evolved from just ‘nice to have’ to ‘how to achieve,’” he adds. “Companies are expecting it, but aren’t willing to accept the cost and risk of doing it alone. We need the community to discuss realistic implementations, identify the most critical elements and bring together the ecosystem partners to build baseline reference architectures for key digital building blocks. The community also needs to assure the reliable flow of data among the electronic manufacturing segments from semiconductor to OSAT to EMS.” Predictive maintenance and virtual metrology applications could mature in next few years While predictive maintenance initially seemed a likely early application of machine learning in factories, it remains a challenge for the electronics sector. “The difficulty is that it’s not clear where to get the most bang for the buck,” says Tom Ho, president of BISTel America, noting that it may make the most sense to track the failure performance of a single expensive part, like an electrostatic chuck, since predicting the failure performance of a whole complex system like an etcher is much harder. “Collecting enough data from all failure types, including especially the rare events, is difficult unless you have a long history of a lot of tools,” adds Doug Suerich, PEER Group product evangelist. “The gain from collecting performance information from many tools across the industry could be big, but many companies still need to overcome concerns around exposing their IP.” Another big opportunity for prediction is virtual metrology – predicting the wafer outcome from the process or sensor data with enough accuracy to replace the physical metrology. “Virtual metrology is improving, and since metrology can be slow and expensive, any reduction could mean a huge potential savings,” says Suerich. “But it is still seen as too scary for many companies. Two to three years from now, companies will expand the practice from lower risk areas into processes that require more confidence in the results.” Moving beyond prediction to automated control needs digital models Once the results are predicted, the model can be used to control or automatically optimize a process and enable the system to learn by itself, usually by reinforcement learning on a digital model. The model can then independently make adjustments to optimize the manufacturing process. “Automated process development is getting close now. Instead of smart guys turning the knobs, deep learning is automating the smart tuning,” says Suerich, suggesting the industry could see widespread adoption in as little as two to three years. This type of machine learning needs a good digital model, and masses of data for learning. One approach uses human experts to build a physics-based model of the clearly understood parts of the process, then turns to deep machine learning to optimize the lesser-understood variables. The alternative, the data-first approach, runs a computer algorithm to suggest the solution purely from data, without human input, and then relies on the human to evaluate the usefulness of the results. Modeling digital twins of wafers could enable automated process control, chamber matching, and fleet matching, says Fried. If every wafer had its own virtual twin with all the upstream metrology and structural information needed to make equipment control decisions, it could feed forward that information to enable the seamless transition from one step in the process to another based on understanding their complex interrelationships. This could potentially improve uniformity across wafers and equipment, and reduce the need for metrology, he argues. Moving metrology sensors into the chamber will also require model-based algorithms to enable dynamic process control in close to real time, says Fried. These algorithms will be needed to acquire, parse, and process the data at high speed, and then to choose how to adjust the controls. “There will be a model behind collecting and interpreting the metrology data,” he notes. “That’s a really rich vein for improvements in process control.” “The end goal is to collect equipment data in real time, analyze it with AI, and send back controls to optimize manufacturing processes,” Jabil’s Gamota says. “This requires a robust architecture for communication between equipment and consistent formats for data collection and analysis. But the cost and complexity of this heavy lifting is too great for any one company to do alone. We need a consensus-based architecture for ingesting, analyzing and acting on the data.” SEMI tests data transfer protocols, benchmarks best practices SEMI is launching a smart data project to identify the various data transfer protocols needed for inter-company communications. The project will feature a proof-of-concept model in a development fab to produce verifiable results so SEMI can better understand how different approaches meet member needs. SEMI’s smart manufacturing technology communities and the Fab Owners Alliance are also benchmarking current smart manufacturing practices in the microelectronics industry to help SEMI members better understand the path forward and potential return on investment. Speakers over all three days at SEMICON West addressing these issues include Active Layer Parametrics, Applied Materials, Applied Research Photonics, ASML, Bosch Rexroth, Cimetrix, Coventor, ECI Technologies, Edwards Vacuum, Final Phase Systems, GE Digital, Infineon, Jabil, Lam Research, Osaro, Otosense, PEER Group, Qualcomm, Rockwell Automation, Rudolph Technologies, Schneider Electric, Seagate, Siemens, Stanford University, TEL, TIBCO Software. See semiconwest.org. What’s next for smarter, more connected electronics manufacturing - Part 1 What’s next for smarter, more connected electronics manufacturing - Part 2 Paula Doe, SEMI

What’s next for smarter, more connected electronics manufacturing - Part 2The fast-maturing infrastructure now enabling applications for big data and artificial intelligence means disruptive change not just at individual companies but also in data connections among companies across the microelectronics manufacturing value chain. SEMI checked in with some leading players on the changes they see coming in the next several years for this article series. The trade group is expanding its programming on smart manufacturing to address these industry-wide developments at SEMICON West, July 10-12 in San Francisco.“The ramp of EUV, and the smaller geometries and smaller process margins, will drive an exponential increase in the amount of metrology data to manage,” says Neal Callan, ASML vice president, Silicon Valley. Callan notes that moving to multibeam e-beam inspection will increase data volume from megabytes per second to gigabytes per second and from thousands of data points to millions of data points. “The process is so tight and the margin so small that stochastic variation, or noise, becomes more dominant – at least it’s noise until we can learn to understand and control it. And understanding and controlling this variation will be key to delivering 5nm patterning,” he says.Single-beam e-beam inspection is already driving large increases in data as engineers extend the slow technology to broad, high-speed defect metrology applications by more intelligently instructing the system where to look for problems. Callan says ASML is now using the scanner data on wafer focus, alignment and leveling. The company is also using the computational lithography model from the design to identify the smallest process windows in the pattern that are most likely to see problems. The model then quantifies the number and significance of those instances.“The collection of all this diverse data means that tools will need to be plug-and-play so all tool data is instantly available to all systems and software,” says Doug Suerich, PEER Group product evangelist. “We need tools that can be discovered automatically by the network so it can start slurping up data immediately. The adoption of the Interface A (EDA) standard is accelerating and fabs are starting to ask for it. The proliferation of sensors also needs to self-discover. If you are going to add thousands of new sensors into a facility, you can’t afford a time-consuming integration process.”“We are now seeing that engineers are greedy for more data – if they can get the data, it’s becoming a need-to-have,” adds Tom Ho, BISTel America president. “Getting more data from more sensors, from the sensors on the tool that are not being fully utilized, and from untapped data sources like vibration is another big coming opportunity.” Process complexity drives demand for feed-forward between silos with computational models ASML co-optimizes its scanner process with etch and reticle process steps. Source: ASML In addition to the drive for trace-back of data, the increasing complexity of interrelated processes is also driving demand for feed-forward of data. “Feed-forward is becoming more important,” notes Ho. He points to the example of 3D NAND features, now getting so deep that identifying the layer being measured is a challenge unless the signal at the step before can be recognized. “We need partnerships with our peers to understand how to take advantage of the sensors they use, integrate them with our data, and then feed-forward corrections to the other systems,” concurs Callan. “To drive the best CD uniformity and overlay, we need to co-optimize litho and etch,” agrees Henk Niesing, ASML director of product management. He notes that the company is working with etcher makers to measure the overlay and CD, decompose the finger prints, and then use models to steer automated control that best adjusts both the scanner and the etcher. ASML is also working with Zeiss on co-optimization between the scanner and the reticle to make even higher-order corrections by locally modifying the reticle.These higher-order corrections, applied on each exposed field, drive the need for even more data, and at higher speed but without higher cost, notes Jan Mulkens, ASML senior fellow. These corrections increase demand for computational metrology, which combines various metrology sources with physics and deep learning models trained on real data to predict and control process results in real time. “We’re working on computational metrology to ideally use all the knobs we have in the fab,” he says. So far this effort has largely involved linking data between two companies. More consistent data formats would enable data exchange to be extended to more companies. “The software versions also need to be managed for upgrades so they still match after one party updates the system on its tool,” notes Niesing. Speakers on these issues of smart manufacturing and data handling at SEMICON West include Active Layer Parametrics, Applied Materials, Applied Research Photonics, ASML, Cimetrix, Coventor, ECI Technologies, Edwards Vacuum, Final Phase Systems, GE Digital, Infineon, Jabil, Lam Research, Osaro, Otosense, PEER Group, Rockwell Automation, Rudolph Technologies, Schneider Electric, Seagate, Seimens, Stanford University, TEL, TIBCO Software. See semiconwest.org.What’s next for smarter, more connected electronics manufacturing - Part 1What’s next for smarter, more connected electronics manufacturing - Part 3Paul Doe, SEMI

What’s next for smarter, more connected electronics manufacturing - Part 1The fast-maturing infrastructure now enabling applications for big data and artificial intelligence means disruptive change not just at individual companies but also in data connections among companies across the microelectronics manufacturing value chain. SEMI expands its smart manufacturing program with a Smart Manufacturing Pavilion with displays and three full days of talks to address these industry-wide developments at SEMICON West, July 10-12 in San Francisco.Autonomous autos’ demand for zero-defect systems and 100 percent traceability back to the manufacturing data for each die is driving a push to traceability across the chip sector. “Far more chips are being used by the automotive sector, and its very different requirements are driving demand for traceability,” says Tom Ho, president of BISTel America. “Our chipmaker customers are looking for traceability solutions and the trend is the same in backend packaging and assembly – automotive applications are driving the sector to traceability.”Traceability is also driven by the growth of systems in a package as fabless chipmakers look to connect back to the packaging companies’ fault analysis labs and die interconnect history to diagnose and fix the cases where known-good die are failing in the system, adds Mike Plisinski, CEO of Rudolph Technologies. Plisinski adds that makers of consumer products like phones that can also see harsh conditions are demanding higher quality and traceability as well. The electronic manufacturing services (EMS) sector also must establish an architecture for traceability to collect critical manufacturing-related data and to interface with OSATs and semiconductor fabs. The reason is that EMS companies are adding traditional OSAT processes such as assembly of products with bare die and complex optics modules requiring clean rooms. “A unified sand-to-smart-phone smart manufacturing roadmap should be established,” says Dan Gamota, vice president of Engineering and Technology Services at Jabil. “We need to identify protocols for manufacturing data communications that can be adopted across the supply chain.”To enable smart manufacturing, vendors need to collaborate on getting their production equipment to interoperate and support factory analytics and data management systems. Source: SEMI One big challenge, of course, is how to format this diverse data so it can be linked and used by various supply chain stakeholders. “Smart data needs to be contextual and it needs data standards across the supply chain so it’s easy to link from the front end to the back end, follow common lot IDs front and back end, and have a way to map streaming data from sensors to a discrete lot ID,” notes Ho. New approaches to metrology, analysis and test that increasingly exploit machine learning on simulations will also be needed to help predict which die and connections that test well now may fail in the future as conditions change.Another issue is how to securely share the needed data across companies without jeopardizing IP. “On the equipment side we collect data across customers on how the tool is running to improve the equipment,” notes Neal Callan, ASML VP Silicon Valley. “Next we need to integrate performance and reliability data that today is not as well shared.”The other big hurdle is how to pay for data sharing. “The challenge is that the final manufacturers reap the benefit of traceability, but since they expect their suppliers to deliver good die, they don’t want to pay more for it,” notes Plisinski. He suggests that over the next two to three years, traceability and predictive fault prevention will become the norm as the automotive sector is compelled to invest in it to assure safety. Meanwhile, fabless companies will face so much complexity in integrating different die from different suppliers in SiP that they will no longer be able to afford to simply use the cheapest supplier, potentially driving a fundamental shift in relations and division of labor among fabless chipmakers, OSATs and fabs. Standards extend across supply chainSEMI member committees are collaborating to build the infrastructure to enable these developments. Standards committees are updating standards for higher bandwidth data exchange and extending semiconductor-like vertical and two-way horizontal equipment communication standards to flow shops to enable assembly players to optimize and trace back results across players. The SMT/PCBA community is integrating its smart manufacturing work into SEMI standards, and the SEMI A1 standard was a key reference document in the development of the Japan Robotics Association’s Equipment Link Protocol.Speakers addressing these issues at SEMICON West include Active Layer Parametrics, Applied Materials, Applied Research Photonics, ASML, Bosch Rexroth, Cimetrix, Coventor, ECI Technologies, Edwards Vacuum, Final Phase Systems, GE Digital, Infineon, Jabil, Lam Research, Osaro, Otosense, PEER Group, Qualcomm, Rockwell Automation, Rudolph Technologies, Schneider Electric, Seagate, Siemens, Stanford University, TEL, TIBCO Software. See semiconwest.org.What’s next for smarter, more connected electronics manufacturing - Part 2What’s next for smarter, more connected electronics manufacturing - Part 3Paula Doe, SEMI

The fast-growing automotive semiconductor market means big change for the IC supply chain. Beyond the obvious demands for reliability and traceability, the sector is moving towards simpler and lower-cost solutions while facing the daunting challenge of automating driving in a complex world. The need for simpler and cheaper automotive intelligence will likely drive acquisitions to build complete platform solutions that are easier to integrate. This demand has already spawned a market for pre-configured test cars to save developers time and money, and is driving LiDAR (Light Detection And RADAR) towards lower-cost, solid state solutions. “The growth of the automotive electronics market provides a great opportunity for the IC supply chain to differentiate on specialty processes and quality for the high-volume automotive business with its long design cycles,” says Scott Jones, principal, strategy, at KPMG, who will speak in the automotive program at SEMICON West. “This differentiation is a chance to reduce chip suppliers’ dependence on scaling volume for the mobile phone world with its short-cycle volatility of winning and losing sockets.” He notes that increasing demand for automotive ICs is also reinvigorating the eight-inch supply chain and spurring opportunity for specialty products such as compound semiconductor devices for power efficiency. Supplying the automotive market also means addressing automotive reliability requirements, which can be 10 times more stringent than for consumer devices. At the same time, the industry must sustain fast-paced development cycles required for the volume and diversity of low-cost IoT devices, manage the segmented supply chain for both those markets, and still spread development costs. Another big challenge for the supply chain will be to automate testing and update vast amounts of embedded software in these automotive devices. “The more complete solution a company can put together, the more the automakers will gravitate to it. They want simplicity,” Jones suggests. Smaller players will need to differentiate with IP and acquire other IP provider to build a broader platform, or be acquired and folded into an all-in-one solution.AutonomouStuff helps accelerate and simplify development of autonomous driving solutionsAutonomouStuff is helping to speed development of these platforms. The company has grown from a sensor distributor into a supplier in the emerging niche of vehicles preconfigured with key interfaces for sensors and controls. These interfaces can then be customized by integrating different components for developers to test their applications. AutonomouStuff offers developers a lineup of vehicle models pre-configured with the interfaces needed to add desired chips, sensors and software to develop their autonomous vehicle systems. Source: AutonomouStuff.“Whether they’re major chipmakers or AI software startups, they don’t have a year to build their own vehicle platforms themselves for developing autonomous vehicle systems,” says Wolfgang Juchmann, VP sales and business development at AutonomouStuff. Juchmann, a SEMICON West speaker, will bring a demonstration vehicle to the show. “In four to six weeks we can prepare a custom test car with selected sensors, enabling users to start testing their computer platforms and software. It’s faster and more cost-effective for us to supply the car with the needed interfaces.” He notes that developers are using some 300 AutonomouStuff vehicles in the field. AutonomouStuff customers are starting to transition from testing on a single car or two to testing on mini-fleets with 50 to 100 vehicles. Beyond sensors and pre-configured vehicles, the next step will be to add more data intelligence services to help with capabilities like tagging the data for training, Juchmann says. AutonomouStuff already offers hardware to support Baidu’s Apollo open-source software stack and data set. The company was recently acquired by the Swedish holding company Hexagon to help support expansion.CMOS silicon LiDAR nears automotive qualificationInnovations in the hyper-competitive LiDAR market, where burgeoning demand is driving the race to develop various types of solid-state devices, may also help reduce the cost of autonomous vehicles. Among the roughly 40 LiDAR suppliers, at least one – Quanergy – is taking advantage of 45nm and 32nm foundry CMOS volume production. The company uses voltage through the semiconductor stack to change the refractive index, controlling the phases of optical beams and the resulting interference patterns of light exiting the chip to quickly steer the laser beam without the need for moving parts, much like the phased array radar its team developed earlier. Solid state LiDAR image with object recognition software. Source: QuanergySo far, most of the small LiDAR units have shipped to the security, industrial automation, drone, robots and 3D mapping markets. However, Quanergy CEO Louay Eldada, another SEMICON speaker, says the company is also winning automotive designs and expects automotive shipments to take off early next year, once automotive certification testing is completed. “We can get design wins because standard CMOS production at TSMC makes us a known entity,” says Eldada. To prevent component misalignment, the company produces its own specialized packaging to secure the laser, phase control ASIC, optical phased-array emitter, detector array, and receiver readout ASIC at its plant in Silicon Valley or the facility of its automotive partner Sensata. Through its software business, Quanergy offers an artificial intelligence (AI) perception program for object recognition and LiDAR tracking. The solution uses the people-tracker software the company acquired from Raytheon.SEMICON West this year expands to three full days of automotive electronics programming and features a Smart Transportation Pavilion. Other companies with experts who will speak as part of the program include XPT/NIO, Infineon, McKinsey, Voyage, GM Cruise, Bosch, Deepen AI, Airbus A3, Nvidia, Excelfore, Byton, Macronix, SK Hynix, SAP, Xilinx, Achronics, California Fuel Cell Partnership, Velodyne, Lam Research, KLA-Tencor, SCREEN, Rockwell, Versum Materials, TechSearch International, Entegris, ASE, Amazon, Continental and Wind River. www.semiconwest.orgPaul Doe, SEMI

With Southeast Asia’s semiconductor industry expected to grow up to 12 percent this year, the stakes at SEMICON Southeast Asia were high. The challenge: to drive industry connection, collaboration and innovation across a broad range of technologies to accelerate growth in the electronics manufacturing supply chain.SEMICON Southeast Asia 2018 delivered.The region's premier gathering of the global electronics manufacturing supply chain, SEMICON Southeast Asia drew an event record of more than 7,500 visitors and over 300 exhibitors to its debut at the Malaysia International Trade and Exhibition Centre (MITEC) in Kuala Lumpur. The Market Trends Briefing, an event favourite, offered insights into the latest trends and developments on forward-thinking topics as diverse as the machine as an integral part of human society (NXP Semiconductors Singapore) and intelligent robots (Festo Robotics). Key industry topics including semiconductor fab investments (SEMI) and drivers and applications for Fan-Out Wafer-Level Packaging (TechSearch International) also highlighted the briefing. 2018 Year to Date Statistics from the Market Trends Briefing For the first time, SEMICON Southeast Asia convened policy makers and industry leaders in a panel – the CXO Speaks session – that provided insights into how the region can strengthen its manufacturing ecosystem, capture new opportunities in IoT, and build a resilient and growing electronics industry. The panelists agreed that the Southeast Asia semiconductor market will continue to grow exponentially in the digital era, and that regional players must not only collaborate to sustain this growth momentum but build a strong talent pipeline to continue to drive IoT innovation.With connection clearly critical to the industry’s growth, the event’s Business-Matching sessions and industry VIP networking brought business leaders together to find new partners and opportunities.Themed ‘Think Smart, Make Smart,’ SEMICON Southeast Asia featured few devices smarter and more innovative than Festo’s AirJelly, a radio-controlled airborne jellyfish. The first indoor flying object with peristaltic drive mimics the movement of a real jellyfish – except in the air. The device’s eight tentacles adapt to its environment, just like its 500-million-year-old sea-roaming cousin. A lithium-ion battery, an electric motor and a bit of helium are its wings, allowing it to take flight.The ‘World of IoT,’ a show-within-a-show, highlighted enabling applications and technologies for the IoT revolution. This interactive experience was helmed by seven Malaysian technology start-ups that showcased present and near-future consumer technologies such as autonomous vehicles, smart AI devices and virtual reality applications enabled by semiconductor innovations.In an effort to attract STEM talent to the industry, SEMICON Southeast Asia for the first time staged a panel with 11 experts from the public sector and seven from the private sector to discuss strategies for encouraging young graduates to pursue engineering careers and building a talent pipeline. For their part, the SEMICON Southeast Asia university programme and the Electronics Talent Career Fair focused on helping to build the global semiconductor industry as it faces a worker shortage.At SEMICON Southeast Asia’s Technology Innovation Forum, thought leaders from across the industry answered the question: What does Smart Manufacturing mean to the electronics manufacturing supply chain? While presenters from GLOBALFOUNDRIES, Amkor, PricewaterhouseCoopers, Infineon, Lam, IBM, Omron, and OSRAM looked at Smart Manufacturing from very different positions in the supply chain, they shared common issues with data sharing and data protection, and decision-making methodologies when monitoring a huge influx of sensor data. Samivel Krishnamoorthy, Director of Digital Manufacturing Industrialization at OSRAM, closed the session with a real-world look at the work necessary to transition a cluster of production lines with different systems to Smart Manufacturing capable lines following common systems and data handling techniques. OSRAM embraces SEMI Standards for use in conventional silicon front-end manufacturing in for Osram’s LED production. Krishnamoorthy detailed an exceptional analysis process to benchmark and adapt best practices to complex, multi-stakeholder technology production environments.Those looking for a highly influential audience from every segment of the global microelectronics manufacturing supply chain found it as SEMICON Southeast Asia. Technology and business leaders from segments including semiconductors, LEDs, MEMS, printed/flexible electronics, and other adjacent industries were a powerful presence at the event.Held for the first time in Kuala Lumpur, the event remained true to its mission to connect electronics industry innovators and thought leaders from business, academia and research from both region and all over the world. SEMICON Southeast Asia 2019 will once again be held at MITEC.Kai Fai Ng is President, SEMI Southeast Asia.