semiconductors

Global business conditions continued to improve through October although the rate of improvement slowed a bit as pandemic concerns increased (Chart 1).Electronic Equipment Shipments RecoveringThird-quarter world electronic equipment shipment growth showed a big improvement over the second quarter but was still down an estimated 1.4% compared to the same quarter in 2019 (Chart 2).Based on regional electronic equipment shipment data, October 2020 sales were up 3.5% versus October 2019 and up 6.1% sequentially versus September 2020 (Chart 3). As the traditional autumn busy season winds down, the key impediment to a strong recovery is the rising COVID-19 infection rates, especially in the United States and Europe. The world awaits the deployment of a much-needed vaccine.Semiconductor Growth May be EbbingSemiconductor chip shipments continue to increase but their global rate of growth has leveled off to mid-single digits (Chart 4). Wafer foundry sales growth also appears to be peaking (Chart 5), pointing to slower chip growth in coming months.SEMI Equipment ShinesSemiconductor capital equipment shipments continue to outshine both electronic equipment and semiconductors. Third-quarter 2020 SEMI global sales were up a whopping 31% compared to the same quarter in 2019 and up 16% versus the second quarter of 2020 (Chart 6). SEMI equipment shipments are definitely outpacing semiconductors on a 3/12 growth basis (Chart 7).SEMI Outpaces Electronic Supply ChainGlobal electronic supply chain growth is improving but the semiconductor sector is clearly the winner this autumn (Chart 8).Looking Forward, Pandemic Spread is Biggest WorryBusiness conditions definitely look brighter. Even stronger growth is likely if we can get COVID-19 under control.Walt Custer of Custer Consulting Group is an analyst focused on the global electronics [email protected].

PNI Sensor, a member of the SEMI-MSIG Positioning, Navigation and Timing (PNT) Technical Advisory Council, is developing advanced tracking systems that promise to increase industrial worker safety.The availability of low-cost GPS jamming and spoofing technologies renders GPS-only solutions for location and navigation an increasingly dangerous and ineffective choice for the dismounted soldier in a battlefield environment. This threat to armed forces has spurred development of new self-contained location and navigation technologies for defense applications — an innovation that offers significant advantages for commercial applications.Though not as complex and mission-critical as in defense, self-contained location technology is also essential in commercially available industrial applications. That’s particularly true for workers in industrial sectors such as utilities, mining, and construction, and in environments with lone or remote workers, such as first responders. While jamming and spoofing are not a threat in the industrial sector, determining the precise location of workers in GPS-denied environments is fundamental to ensuring their safety. This makes it a priority to adapt any self-contained, non-infrastructure-based location technology — which was first developed for the modern dismounted soldier — to industrial applications.Bodies in MotionInertial solutions are very difficult to implement properly, even without the challenges uniquely created by human motion dynamics. On a construction site, for example, workers tend to cover a wide range of disciplines: supervisors, electricians, iron workers and equipment operators, among others. While performing their jobs, construction workers change locations, both indoors and outdoors, and perform dynamic motion such as crawling, ducking and climbing. These are all motions that are very difficult to model using traditional adaptive filtering techniques, which are typically applied in vehicular inertial navigation platforms, such as aircraft, ships and tanks. Even if existing inertial navigation systems could be made size, weight, power and cost (SWaP-C)-compatible to be body-worn, their performance accuracy would still need to satisfy the application’s requirements. To properly determine a worker’s precise location to ensure safety on job sites and in remote locations, we must tackle the combined challenges of SWaP-c and human dynamic motion. That’s the most effective approach for creating a complementary positioning technology that augments GPS or other infrastructure-based location systems.To address these challenges, we need to build a high-performance inertial measurement solution using commercially available MEMS inertial sensors. The issues of bias drift error and low sensitivity have traditionally made such sensors practically useless for any meaningful inertial tracking. Fortunately, this is no longer the case. We now have sensors that already conform to the necessary SWaP-C requirements for the application, and have the additional advantage of high dynamic range of measurements without saturation errors, which helps to reduce high-force and rapid movement-induced errors, promoting greater accuracy.Thus, a path forward is emerging. The current generation of high-performance MEMS gyros can now inertially track workers’ locations to step-level resolution very well for up to 30 minutes — without significant location errors due to bias or scale errors. That’s an order of magnitude better than previous generations. With the new MEMS gyros, errors typically remain less than 2% of distance travelled over that time period. Strategically applying algorithm improvements with higher levels of magnetic corrections has the potential to bring that accuracy down even lower, to less than 0.5% of distance traveled for durations of one hour or more. What’s more, the improved gyro and accelerometer bias, gain, and signal-to-noise (SNR) performance allows for better magnetic anomaly rejection. This enables finer and more sustained gyro bias corrections in the fused solution, which creates a system greater than the sum of its parts. We believe that these newer systems will promote greater worker safety at a truly affordable price.PNI Sensor, a member of the SEMI-MSIG PNT Technical Advisory Council (TAC), is developing a tracking system that combines the best elements of the newest-generation MEMS devices with an electronic compass that uses advanced magnetic anomaly detection and rejection algorithms. Based on PNI’s latest attitude and heading reference system (AHRS), the novel PNT system employs a unique Kalman algorithm that intelligently fuses its reference magnetic sensors with gyros and accelerometers. In conjunction with this work, PNI Sensor has developed advanced pedometry functionality for use in its tracking system for very high dead-reckoning tracking performance used in defense industry applications. PNI is initially designing that system to track dismounted soldiers and special forces operating in GPS-denied or contested environments.For more information about PNI Sensor’s advanced location and navigation technology, please visit PNI Sensor. To learn more about the SEMI-MSIG PNT TAC, please contact Carmelo Sansone, director, MEMS Sensors Industry Group.George Hsu is a founder and CTO of PNI Sensor. He has focused his career on the sensor industry, having invented several magnetic sensor breakthroughs, including the magneto-inductive technology, the core of today’s electronic compassing in the automotive, consumer, scientific and military markets. Hsu is a graduate of Stanford University School of Engineering, holds several patents, and is a much-published author of technical articles on sensor theory, design and applications. He is an active member of the MEMS Sensors Industry Group PNT TAC.About the SEMI-MSIG Positioning, Navigation and Timing ProjectMEMS Sensors Industry Group (MSIG) created a member-based PNT TAC to identify and pursue PNT system innovations for GPS-denied environments. To that end, MSIG solicited proposals from its membership for the SEMI-MSIG PNT Project, a U.S. Army Research Laboratory-funded R D project. PNT committee members that have secured funding are pursuing R D platforms that improve accuracy and performance. Platforms may include software, hardware, and advanced packaging requirements of optical and MEMS-based positioning and timing systems.

Call it a wild guess, but I suspect I am not the only follower of the automotive industry who is tired of reading articles that lament the impact of Covid-19 and speculate, to varying degrees of accuracy, what kind of recovery is in store for major automotive markets around the world.I’m much more interested in what solutions and creative approaches people, companies, and countries have come up with to make cars smarter and safer despite the pandemic or even because of it.A friend of mine who works at a major European vehicle OEM told me that “innovation cannot, must not stop – despite current difficulties.” This sentiment echoes through the automotive supply chain, particularly in the resilience of the semiconductor industry during these challenging times.The recent publication of the AspenCore Guide to Sensors in Automotive – Making Cars See and Think Ahead is a refreshingly positive and inspiring collection of articles, interviews, technology deep dives and business news, all carefully curated and edited by AspenCore Global Editor-in-Chief Junko Yoshida.One article I particularly enjoyed was her “6 Trends on ‘Perception’ for ADAS/AV.” The insights she was able to gather from experts attending the AutoSens show in Brussels are fascinating, even if consensus on what, exactly, will be the winning “robust perception” solution appears to be far off. This is only fitting with so many companies elbowing for that prime spot!Another feature article that stood out was Nitin Dahad’s “Level 5 AVs Unlikely Before 2035” article. It wasn’t so much the longer ramp to full autonomy that caught my eye but the daunting challenge the automotive industry and AVs have to tackle: “…all possible unusual driving situations under all driving conditions and in all environments.” This is truly a mind-boggling undertaking. The author argues that the road to Level 5 “is likely to be paved gradually, as more advanced driver-assistance features come to market.” Sounds reasonable.Both these articles point to the need for collaboration across the automotive electronics supply chain in order to not only sustain the pace of innovation, but accelerate it, as we face our current challenges. This made me think about the SEMI Smart Mobility initiative and how the great minds supporting it might be able to help. The initiative is designed to bring together automotive OEMs, Tier 1s, device makers, design houses, equipment and materials companies as well as R D institutes to address shared challenges and opportunities.SEMI used to stand for Semiconductor Equipment and Materials International, but over the past several years – and driven by the advent of IoT, AI, and everything “smart” – we now represent the entire electronics manufacturing and design ecosystem, with more than 2,400 member companies on our global roster. We created the Smart Mobility initiative in late 2017 with the initial goal of connecting a substantial number of members to new business opportunities involving rapidly rising silicon content in automotive. IHS Markit projects automotive semiconductor revenue to continue to grow at a 6% CAGR to 2026.Over the past 2 ½ years, the initiative has quickly evolved into a global platform connecting the semiconductor, sensor and automotive electronics ecosystem under one roof – the Global Automotive Advisory Council or GAAC. While “silicon content” is still the operative word for many of our core members, the Council’s mission is to address opportunities and challenges that impact more than one segment of the value chain. For example, the challenge of getting to zero defects involves just about every stakeholder – from contamination control in wafer carriers to ensuring device reliability and robustness to packaging and, ultimately, system integration in the car.SEMI also encompasses a number of Technology Communities that provide deep technical expertise in support of the GAAC’s mission. Member companies in our MEMS Sensors Industry Group (MSIG) are directly engaged in and contributing to the GAAC work. GAAC Europe Chapter - Participating Companies“Sensorizing” – making things smarter through the application of sensors – has created solutions for the automotive and mobility space that bring innovation, safety, security and comfort to driver and passenger and that benefit the environment around the car.This makes the AspenCore Guide to Sensors in Automotive a great resource for our members and SEMI staff as we collaborate to accelerate the drive toward Level 5 autonomy.If you are interested in learning more about SEMI’s Smart Mobility and the GAAC, please contact Bettina Weiss, Chief of Staff and Global Smart Mobility Lead at [email protected] with permission from EE Times.

I recently spoke with Chan Pin CHONG, Executive Vice President and General Manager of Products and Solutions at Kulicke Soffa, about how smart manufacturing is driving new production efficiencies in the semiconductor industry. During our conversation, he also provided practical steps for factory operators to follow in evaluating their smart manufacturing needs in order to ensure successful implementation and discussed the potential payoffs. Based in Singapore, Kulicke Soffa is a leading global provider of ball bonding, advanced packaging, wedge bonding, and electronic assembly equipment for the semiconductor, power and automotive industries.Ng: Industry 4.0 and smart manufacturing are critical to the growth of the semiconductor industry. What does the smart manufacturing movement mean to you or Kulicke Soffa?Chong: The future of smart manufacturing is the vision of building a digital connected factory to drive new manufacturing efficiencies by combining physical and cyber technologies. Industry 4.0 integrates discrete systems and harnesses the power of large volumes of data to move towards greater automation.At K S, we define smart manufacturing across the following four key areas embedded in our roadmap for all K S products, from wire bonders and advance placement tools to pick and place machines: Interoperability – This is about machines, devices and sensors connecting to each other. In fact, the very basis of smart manufacturing is that all devices are connected. Information transparency – Through simulation, various artificial intelligence (AI) tools use contextual information to emulate the actual world. Technical assistance – Robots or machines support humans in making decisions or solving problems. Autonomous decision-making – This is our vision that robots or machines can learn from machines to make decisions on their own. Ng: Please elaborate on some of these areas and how they’re the relevant to smart manufacturing. Chong: The need for machines, devices and people to communicate with each other forms the basis of connectivity, the idea of all machines communicating with each other or a host. Connectivity protocols now in place for machine-to-machine connectivity include SEMA, SECS/GEM, SEMI-ELS and IPC-CFX. Machine technology uses various sensing technologies. For example, for a pick and place machine such as SMT platform on K S Hybrid, the algorithm to recognize and align processes is part of the sensor needed in each machine before to can process and add thousands of components to the substrate or panel. In a wire bonder, the ultrasonics or EFO signal can provide some form of sensing technology for a machine to detect process conditions. Importantly, these sensing technologies can be used to collect feedback for process improvements.One example of how K S machines are connected to the host is our use of an intermediate server or host named KNeXt to connects to all assembly equipment in the fab. The equipment can then, in turn, connect to an external secured cloud or K S Global Cloud.Ng: What are the objectives for smart manufacturing?Chong: The ultimate goal is to achieve higher factory productivity or better OEE (Overall Equipment Effectiveness) by improving machine yields, productivity and efficiency. The key is to leverage AI, 5G, the Internet of Things (Iot) and other industry 4.0 technologies to drive automation and process improvements. Ultimately, each factory must meet productivity, yield and cost goals. Smart manufacturing enables factory operators to meet these goals. That is the focus of smart manufacturing.Ng: What is the biggest potential benefit of smart manufacturing?Chong: Smart manufacturing uses data to predict outcomes of a process step or machine operation. Once data is available in the global cloud, analytics can start to build data sets to run statistical modelling and examine factory operation trends. We can also use the data to identify past machine behaviors in order anticipate outcomes, including undesirable ones that we can then prevent.In the SMT example, if we can systematically examine days or weeks of historical performance, we can plot some statistical variations in the process specifically to a pick or placer or a robot and anticipate or avoid problems. However, all sensors must be in place in the bond head or the robot so that we can detect changes or variations in the robot’s movements.Kulicke Soffa smart manufacturing facility Ng: What are some recent factory improvements smart manufacturing has enabled? Chong: Kulicke Soffa has contributed to the hierarchical architecture of the smart factory and key technologies. COVID-19 is driving demand for greater factory connectivity, and K S offers solutions that are key to remote management and full control of smart equipment from a central control and embedding Internet of Things (IoT), big data, cloud computing and sensors in manufacturing. Using these technologies, a small smart factory can be remotely operated and managed.With COVID-19 limiting air travel around the world and access to support engineers, the need has grown for remote machine access to reduce the downtime per machine. Remote factory access enables off-site engineers to remotely identify and diagnose machine problems.Ng: What are barriers to faster adoption of smart factories?Chong: While most smart factories are capable of network connectivity and data collection, a key challenge is the lack of a business model for smart factories and smart equipment. Most factories must justify major capital investments by demonstrating ROI (Return of investments) potential. Capital improvements for every factory usually take several years to implement and are based on a complex business model. Factory connectivity requires substantial investments and years to implement. The same is true of the cloud infrastructure buildouts necessary to generate big data and meaningful analytics. The executive mandate for factory management to install capability usually calls for specific business targets in the planning stage.Another longstanding barrier to entry is the lack of compatibility of existing tools with new factory protocols, raising the question of whether the cost of replacing legacy tools justifies the need for a smart factory. If new factory investment is required for the latest tools to support the production of new products, the ROI will be much easier to justify.Ng: How is AI is important in smart manufacturing?Chong: AI interprets and learn from data to perform tasks and meet specific goals. Good examples of AI implementations include Amazon’s Siri and Alexa voice-command devices and self-driving cars being developed by Google and Tesla.At K S, over the years we’ve implemented AI in our smart wire bonders to reduce human intervention in our ProCu-7, PSP-2, ProCu Loop 2, Pro Bump and overhang processes.Thanks to AI, with senses of signals from the bonder, we can reduce the amount of parameters that an engineer or technician have to do trial and error. With on bonder metrology, PBI, loop height, wire sway features, AI allows us to measure process efficiency and provide feedback.Over several years of AI development, we have leveraged the technology to monitor machines and provide real-time performance feedback in order to provide better closed loop control such as short tail recovery in our bonder process. We can also use the data to predict machine behavior, monitor its health and track maintenance. Ultimately, AI enables fabs to improve manufacturing efficiency, productivity, yields and device quality.Ng: What’s an example of how AI has solved your manufacturing equipment problems?Chong: We’ve used AI to set RPM (real time monitor) limits, identify defective P-parts and monitor various conditions such as wire size and capillaries. These types of cases can arise in any manufacturing environment as humans make process mistakes or use the wrong part for a machine. With AI, we can prevent these problems and reduce the risk of further material lost from the wire bonding process.Ng: What advice do you have for factories looking to implement smart manufacturing systems?Chong: To build a smart factory, start by focusing on a clear set of business objectives and how smart manufacturing will help minimize or eliminate current factory inefficiencies. In other words, start with the end in mind – the problems that needs to be solved and the business goals – and identify the information you need to demonstrate ROI. Do you need to resolve, automate or improve processes or just to be more efficient? Before investing millions or billions of dollars to build a smart factory, identify those clear goals upfront. Then map out the particulars of implementation to avoid major problems around standards, protocols and connectivity.Bee Bee Ng is president of SEMI Southeast Asia.

Europe is facing an acute shortage of skilled microelectronics workers that undermines the growth potential of not only the electronics industry but the European economy as a whole. Nearly 1.1 million job advertisements for electro-engineering workers were placed in the EU between mid-2018 and the end of 2019 (CEDEFOP, 2020). The shortfall looms large as a skilled and diverse workforce that can continuously innovate is the oxygen of microelectronics. In light of the critical importance of microelectronics to Europe’s ability to fulfill its growth potential, SEMI Europe participated in the high-level roundtable hosted by Commissioner Nicolas Schmit and Commissioner Thierry Breton on October 5. The discussion’s key takeaway: The skills challenge facing the microelectronics industry is too complex for one organization to tackle, and reskilling and upskilling its workforce should be a common priority for Europe. Only with a diverse, substantial and skilled microelectronics workforce can Europe achieve its R D, design and manufacturing ambitions while ensuring its sovereignty in the digital age. The roundtable highlighted the EU Pact for Skills as a key means to narrow the industry’s skills gap.An ever-growing part of our lives, microelectronics, with their ability to run billions of computations per second and store vast quantities of data, are the brains of modern technology. The digital sovereignty of nations around the world today relies on advanced microprocessors to collect, transfer, analyze and store immense amounts of data used in key end-user sectors such as mobility, telecommunications, energy, security and healthcare. Information and communication technologies (ICT) enabled by microelectronics are helping much of the world’s population to work and study from home and remain safe during the COVID-19 pandemic.According to the Smarter2030 Report, further deployment of ICT, including electronic components in critical sectors such as transportation, manufacturing, agriculture, construction and energy, could eliminate the equivalent of 12.1 billion tons of CO2 per year globally. These are some of the reasons why nations worldwide are making large-scale investments to advance a homegrown microelectronics R D, design and manufacturing base. It is no surprise, then, that semiconductors are now at the center of the so-called global techno-trade wars.Clearly, Europe urgently needs to mobilize and pool resources to develop effective lifelong learning programs for all workers and continue investing in microelectronics innovation. We need to instill the passion for creating technology among current and future workforce, in particular women and people with challenged backgrounds, and build a highly diverse talent pool. Working together, we can better demonstrate how computing technologies, including quantum, high-performance and edge AI, provide solutions to grand societal challenges and attract talented people to the fascinating world of electronic components and systems.Against this backdrop, the microelectronics industry finds the Pact for Skills very timely and crucial to advancing the talent pool underpinning Europe’s deep digital ecosystem. The Pact will play an instrumental role in improving the scope and the quality of training partnerships at regional, national and European levels, sharing best practices and helping the microelectronics industry and workforce adapt to the effects of COVID-19.The microelectronics industry is committed to building on the momentum created by the METIS Erasmus+ collaborative project and to mobilizing our ecosystem and education partners for a successful Pact for Skills in Microelectronics starting this year.The High-Level Roundtable: Skills for Microelectronics was hosted by Commissioner Thierry Breton and Commissioner Nicolas Schmit. Participants included Paul Boudre, CEO, SOITEC; Lars Reger, CEO Germany and CTO, NXP; Frits van Hout, Executive Vice-President and Chief Strategy Officer, ASML; Françoise Chombar, CEO, Melexis; Emmanuel Sabonnadiere, CEO, CEA-Leti; Luc Van den hove, President and CEO, imec; Sabine Nietzsche, Board member, Silicon Saxony and Vice President, GlobalFoundries; Laith Altimime, President, SEMI Europe (coordinator of METIS); Yolande Berbers, President, European Society for Engineering Education (SEFI); James Calleja, President, European Forum for Technical Vocational Education and Training (EFVET); Ludovic Voet, Confederal Secretary, European Trade Union Confederation (ETUC).Emir Demircan is director of Advocacy and Public Policy at SEMI Europe. To learn more about SEMI Europe advocacy, contact Emir at [email protected].

The historic and forecast growth trends for cleanroom robots are in line with the long-term growth rates of semiconductor manufacturing equipment.

Connectivity. Electrification. Shared Mobility. Autonomous Driving. McKinsey Company cites these four disruptive trends behind future mobility — dynamics that could help to transform quality of life for hundreds of millions of people.McKinsey Company predicts that by 2030, mobility innovation could dynamically alter everything from safety in human locomotion to air quality, public spaces and power systems. Much the same way that tiny plankton in our oceans sustain aquatic animals, MEMS and sensors, while small, are crucial building blocks of integrated mobility.As partner at McKinsey Company, Andreas Breiter will explore this connection during his MSEC 2020 presentation, Future Mobility Enabled by Sensorization. SEMI recently caught up with Breiter to preview his October 7 talk at SEMI’s first virtual MEMS Sensors Executive Congress, October 6-8 and 13-15, 2020.Register now for MSEC 2020 and explore this topic with Breiter during the live Q A portion of his presentation.SEMI: You play a dual role at McKinsey Company, advising clients in advanced industries on capital investments and serving on the leadership team of the McKinsey Center for Future Mobility (MCFM). What is the relationship between them?Breiter: Mobility has become so much more than the auto sector. Today when we say future mobility, we’re talking about the convergence of many exciting developments influencing the ways that people and goods move around. Cars have become computers, and we now have to contemplate new frontiers, such as air taxis and electric vehicle infrastructure.Mobility is changing so quickly that it’s inspiring decision-makers from other market sectors to explore what implications it will have for them. We’re helping mining companies think about their haulers, retailers think about their footprints, and insurance companies plan for autonomous vehicles. The MCFM exists as a global think tank to focus on these frontier topics, helping to ensure we are ready for the future. During my MSEC presentation, I’ll explore how those future topics are influencing automotive mobility in the short- and long-term. The MCFM is even more forward-looking, so we’re just starting to build scenarios for what might come in 2040 and beyond.SEMI: How are changes in the mobility ecosystem affecting the automotive value chain?Breiter: In the past, the automotive value chain was clearly structured. We had sensor companies selling to Tier 1 suppliers, who would in turn sell to OEMs, who would sell directly to end customers.The value chain has grown more complex, however. In the future, we might see fleets of robotaxis, which will be owned by companies instead of by individual consumers. Already today, rideshare companies are game-changers because consumers can travel by car without owning one.Plus we see companies offer parts of the user experience such as user interfaces for automotive infotainment. In the past, everything in the car was branded by the OEM, but now we have third-party platforms that let us control some of our automotive infotainment options.SEMI: How are MEMS and sensors suppliers participating in this new value chain?Breiter: The pervasive use of sensors in cars has driven automotive OEMs and Tier 1 suppliers to work directly with suppliers, whose close involvement eases the complexity of integration. Just think about the sensors used in autonomous driving. Getting that right is safety-critical.We’re also seeing suppliers go beyond the individual component level to provide complete systems-level solutions. Advanced driver-assistance systems (ADAS) are a good example.SEMI: Automotive applications tends to have some of the longest design-to-delivery cycles in industry. Will this ever change?Breiter: The automotive product lifecycle was typically five-plus years, with a few years of development before that and continued service after the end of the lifecycle. That gives MEMS and sensors suppliers a 10+ year timeline on one model.With so much innovation taking place, this slow cycle won’t work forever. Over-the-air (OTA) updates, for example, enable new features when they become ready for deployment. I expect we’ll see OTA updates from many end manufacturers in coming years. SEMI: What changes do you foresee in ADAS and autonomous driving?Breiter: ADAS and autonomous features will become much more common. We’ve already witnessed this progression, with introductions first in premier models and later rolling out in more affordable vehicles. Lane-change assist and rear camera followed this path and are now pretty standard. Collision avoidance, as a safety-critical feature, is likely next in line for more widespread adoption.As for fully autonomous driving, consumers will accept that only when it becomes safer than a human driving a car.SEMI: Where is the greatest opportunity in the next five years?Breiter: Electrification of vehicles is number one. When it comes to engines, we’re moving from internal combustion to hybrid and then to electric. Since OEMs are adding sensors for the battery system, for battery management, and for electric motors, this progression represents growth opportunity for sensors suppliers – in particular for hybrid vehicles that contain both powertrain technologies.But that’s not all when it comes to sensors. Outside of powertrains, new sensors are added to enable a variety of functions, including, for example, ADAS and autonomy, as well as increased interior content, such as mood lighting.SEMI: Is there anything surprising coming, sensor-wise, in mobility?Breiter: To enable intelligent traffic systems, you need to make infrastructure smarter — which brings us to sensors. We’re going to see roads and other assets in infrastructure sense the state of traffic, sense what traffic participants are doing, and support connectivity between, for example, the infrastructure, vehicles on the ground, pedestrians on walkways and drones in the air.SEMI: What would you like MSEC attendees to take away from your presentation?Breiter: We’re living in a transformative era for the mobility industry. During the last 100 years of mobility, the ecosystem barely changed. In recent years, however, we’ve seen massive technological gains, largely enabled by semiconductors, MEMS and sensors. Instead of serving as just one of many suppliers, I’d encourage MSEC attendees to anticipate future mobility challenges so they can offer solutions to OEMs and Tier 1 suppliers accordingly.For more information, visit McKinsey Center for Future Mobility. MEMS Sensors Industry Group® (MSIG), a SEMI technology community that connects the MEMS and sensors supply network in established and emerging markets, enables members to grow and prosper. Visit us today.Andreas Breiter leads McKinsey’s capital-investment work for advanced industries in North America as well as its Center for Future Mobility on the West Coast. In his advisory work, Breiter serves a broad range of companies in the automotive sector, including car and truck manufacturers and their suppliers, as well as companies in the utilities and renewables space. He helps executives make strategic choices around product development and helps companies stay ahead of emerging trends, such as autonomous driving, connectivity, electric vehicles, and shared mobility.Andreas holds a Ph.D. in Operations Management and studied in Germany, France, the U.S. and Canada.Nishita Rao is product marketing manager at SEMI.

On 21 September, SEMI and a coalition of 40 industry organisations sent a letter to European Commission President Ursula von der Leyen calling for decisive action to solve implementation issues within the European Union Waste Framework Directive, specifically the Substances of Concern in Products (SCIP) database.The signatories, who represent a very significant part of the European economy, are requesting urgent resolution of implementation issues for the SCIP database, which is designed to support the circular economy as defined in the European Green Deal. The database is required under Article 9.1 of the updated Waste Framework Directive.In the letter, the signatories ask President von der Leyen to take immediate action to: Postpone the SCIP notification deadline of 5 January 2021 to at least one year after finalization of the database; Conduct a study on the usefulness, feasibility, proportionality and impact of the database; Instruct the European Chemicals Agency (ECHA) to adapt the SCIP database according to the outcome of the proposed study. ECHA failed to complete development of the database by the January 2020 deadline required by the Waste Framework Directive, leaving companies insufficient time to develop, test and adapt their own systems to meet the January 2021 SCIP notification deadline.Over the last two years, the signatories have repeatedly shared their serious concerns regarding the viability, proportionality and value of the SCIP database with the European Commission and the ECHA, yet those concerns remain unresolved.Contrary to the EU Better Regulation principles that call for open and transparent decision making, Article 9.1 was added to the revised Waste Framework Directive during the final stage of the co-decision process without any prior stakeholder consultation or impact assessment. A proper impact study should help shape the way forward to deliver on the EU ambition of driving a circular European economy.Coalition PartnersEmir Demircan is director of Advocacy and Public Policy at SEMI Europe.

The costs of production are typically based on labor and materials and define manufacturing expenses. But is this approach accurate enough? What about the cost of poor quality and lack of efficiency in production? How is the pandemic impacting semiconductor manufacturing and what can we expect from the future?SEMI recently spoke with Dr. Eyal Kaufman, founder and CEO of QualityLine, a Kiryat Gat, Israel-based provider of smart manufacturing analytics solution, about manufacturing controls and how to select the best data source to improve product quality and yield. Kaufmann provided a snapshot of current best practices used by the company to improve manufacturing efficiencies and product quality while reducing costs. He also discussed the COVID-19 pandemic’s impact on semiconductor smart manufacturing and how artificial intelligence (AI) can help keep factory workers safe.For additional insights on smart manufacturing, join the virtual SEMI Global Smart Manufacturing Conference, October 20 - 22, 2020. Registration is open.SEMI: Real manufacturing costs are calculated based on different aspects such as failures in production, repairs, products returned, scrap of components or late deliveries. Lack of quality and efficiency in manufacturing can undermine a business. How are you helping businesses overcome these challenges?Kaufman: To increase profit margins, it is essential to identify inefficiencies and what improvements to prioritize. Once manufacturing quality and efficiency deficiencies have been measured, the next step is to continuously collect manufacturing data in order to run the final cost analysis and use the analytics to improve the manufacturing process.Smart manufacturing makes it possible to detect anomalies in automated factories, improve production performance and increase profitability. Today, automated data are collected from every machine and piece of test equipment in the factory. Still, manufacturing data collection in many industries remains manual and expensive because of the time and human resources involved. A real-time analytics system can automatically collect all data sources and select the relevant data for analysis, which today is the most accurate and effective way of measuring and resolving quality and efficiency deficiencies.Data-driven decisions made by smart manufacturing reduce costs and improve manufacturing strategies, enabling factory operators to increase product quality, drive higher production capacity and enhance product design for manufacturability. Analytics solutions monitor shop floor operations accessing vendors and subcontractors’ products criterion to run root cause analysis. All those data will reduce the return rate of faulty products and accelerate return on investment. This is why we definitely need smart manufacturing technologies!SEMI: Data accumulated during the manufacturing process includes vital information about failures, anomalies and machine usability. What data are necessary to create the best analytics solution?Kaufman: Many companies today run data mapping and automatic creation of data capture. They often wonder if they need to use testing data, sensors data or product design data, or whether they should collect feedback from their customers and vendors. The best way to create an effective manufacturing analytics system is to use data sources such as: Feedback from customers (returned units, customers complaints, etc..) Testing data from automated test equipment and manual test activities Feedback from technicians repairing faulty units Analysis of testing processes done by vendors Sensors data Data from our ERP/MES systems Artificial intelligence enables any type and size of data structure, even accumulated data, to be automatically integrated and interpreted. AI-based analytics can also establish correlations between each manufacturing stage to help factory operators quickly conduct deep diagnostic and root cause analysis for problem solving and prevention – all while leaving intact a factory’s existing process, machinery and data output. Machine learning evaluates how a factory runs its database and puts all the information generated into an analytics solution that provides the know-how to continuously improve factory efficiency.SEMI: How do you select the best data source to improve manufacturing quality and yield? Kaufman: The accuracy and integrity of data accumulated in our manufacturing process is key to controlling and improving yield and quality while reducing manufacturing costs. Smart manufacturing is a technology-driven approach that uses digital and remote connected machinery to monitor the production process. The goal is to identify anomalies in manufacturing processes and leverage analytics to improve process yield and product quality.To select the relevant data, we collect each type and source of data that can improve the efficiency of a real manufacturing cell: Test data from Automated Testing Equipment Test data from Manual Testing Processes Analyses of repairing processes (failed units during the manufacturing process and units that were returned from customers) Once the data structure is collected, the next step is to turn it into actionable information in the manufacturing process. QualityLine smart manufacturing solutions provide a complete one-stop solution to interpret any manufacturing data structure. Our advanced manufacturing analytics solution detects quality and yield anomalies to reveal production line inefficiencies and opportunities to improve manufacturing quality and efficiency.SEMI: How would you describe your approach?Kaufman: Industry 4.0 in manufacturing claims to be the fourth generation of the industrial revolution. Advanced technologies like manufacturing intelligence and machine learning can efficiently achieve zero defects on manufacturing lines. Digital factories leverage technologies and methodologies including: Big data Self-optimization Self-configuration Self-diagnosis Cognitive and machine learning Smart manufacturing technologies enhance the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain high quality performance. The goal is to achieve a significant increase in efficiency and yield while reducing waste and inefficiency.Until now, there has been no viable way to integrate all saved manufacturing data into a unified database. QualityLine advanced manufacturing analytics make it possible for any factory to become digital without installing new hardware, which can be expensive and require not only the extensive integration of existing data but investments in training. Our user-friendly solution integrates manufacturing data for industries with zero automation by first collecting and analyzing data from any type of manual test procedure and then integrated it into manufacturing analytics to improve efficiency.SEMI: Why are Pass/Fail criteria insufficient for controlling manufacturing yield and quality?Kaufman: Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before products can ship to customers. At QualityLine, we establish a test process for each stage of the production flow, from the incoming raw material to the final stage prior to the delivery of finished goods to the client. To prevent unexpected downtime incidents, waste and defective products, we collect and interpret every type of relevant data and turn it into meaningful information, setting up the following capabilities: Collection and interpretation of test and process data of each single unit and from each process and plant Automatic detection of quality and yield problems Accurate and quick root cause analysis process Automatic alerts to abnormal issues Prediction process potential and level of failures Measurement of key performance indicators Many manufacturers base their test criteria of each parameter on one key indicator – Pass or Fail. If the test result shows a Pass, then the unit is ready to move on to the next manufacturing stage. If the test result shows Fail, then the unit is sent to a technician for further analysis.A simple Pass or Fail criteria for product quality is far from sufficient since it provides little or no information about edge cases, where one or more of the technical parameters of the unit under test is only within its allowed tolerance. Edge cases may lead to unit failure during operation such as in extreme environments (cold, heat, humidity, electrical overload, impact, etc.). In fact, when running a mass manufacturing line, it is impossible to continuously digest all the detailed information collected from testing stations. Data is analyzed in detail only when a critical quality problem emerges and further analysis is required to understand the root cause.Information overload and the disregard of important parameters makes it hard to control the process and improve quality and yield. New technologies make fast and scalable data integration possible so data can be collected in real time to detect quality issues early, identify complex process disruptions to avoid delivery delays and ensure the best possible product for customers. Only by accurately analyzing data as actionable information can factory operators control the manufacturing quality process.SEMI: How has COVID-19 impacted the smart manufacturing market? How has your technology helped factories remain online?Kaufman: Smart manufacturing is playing a significant role by helping manufacturers overcome COVID-19 challenges such as workforce reductions, social distancing, drops in sales for some specific products and extreme pressure to cut operational costs.Manufacturing leaders turned to us for a solution to the challenges of maintaining efficient factory operations with a limited workforce and reduced number of operating hours. Filling factory orders with fewer people on the floor is a struggle. Digital factory technologies enable remote monitoring of operations to increase efficiency and capacity. We are helping our clients improve efficiency while reducing costs. Our remote monitoring technology can provide the operational visibility to floor managers and engineering teams who cannot go physically to the factories due to safety restrictions. With our advanced manufacturing analytics, they have full end-to-end visibility and can remotely diagnose and solve production line issues. During this critical time, we are proud to be improving remote monitoring solutions to help the industry withstand the pandemic. Some of our clients would have closed their factories otherwise. We’ve been working to integrate manufacturing data in factories that were previously unautomated to drive high automation levels. Integrating processes with existing factory data, regardless of customer’s protocols or automation level, is our great technology advantage.SEMI: How will manufacturing and its supply chains look after COVID-19?Kaufman: Smart manufacturing is currently a necessity. We collect and analyze data not only to improve quality but to reduce client returns of faulty products by 50% and reduce waste by 22%, both critical points. Manufacturing challenges will continue to accelerate advancements in technology and improve efficiency, safety and productivity as more factory operators incorporate real-time data analytics and artificial intelligence (AI). SEMI: Will suppliers continue to explore new avenues for smart manufacturing technologies and what are their growth opportunities?Kaufman: Yes, definitely. The sector has already changed, with COVID-19 bringing both opportunities and challenges. Industry leaders are facing new pressure, with sudden materials shortages, drops in demand and worker unavailability. The growth opportunities for manufacturing are likely to be digital, as already evident in the immediate response to the crisis. Industry 4.0 solutions will be crucial to increase end-to-end supply-chain transparency, automation and data integration. QualityLine manufacturing analytics have improved key manufacturing performance metrics. For example, based on customer feedback, we’ve increased production yield by 30%, saving some of our customers millions of dollars. Improvements like this can help suppliers withstand pandemics.Dr. Eyal Kaufman, Founder and CEO at QualityLine, has senior management experience and over 25 years of expertise in business development, marketing, finance, operations, engineering and quality management at leading industrial companies. Prior to QualityLine, he served as VP of Mobileye, Cardo Systems, and Medisim Ltd., as well as CEO of OnTheGo Systems. Eyal holds a Ph.D. from California Intercontinental University, an MBA from City University of New York and a BSc. from the Technion in Israel.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness and interest about smart manufacturing with focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality, productivity and cost improvements through smart manufacturing. Activities are focused on building out core capabilities to enable smart manufacturing across the microelectronics supply chain.MADEin4 is a consortium of 47 partners from 10 countries connecting the full range of supply chain: from semiconductor equipment manufacturers and system-integrating metrology companies to RTOS and key applications such as the automotive industry. The MADEin4 Project develops next generation metrology tools, machine learning methods and applications in support of Industry 4.0 high volume manufacturing in the semiconductor manufacturing industry.Serena Brischetto is a senior manager of marketing and communications at SEMI Europe.

Solving challenges in semiconductor manufacturing requires an ongoing collaborative effort by customers, device makers, equipment and materials suppliers, and academia. ASMC 2021 will continue efforts to help the industry overcome these hurdles. To that end, we are now soliciting abstracts from industry experts across all areas of semiconductor manufacturing for presentations at the event, May 3-6, 2021 at the Saratoga Hilton/Saratoga Springs City Center in Saratoga Springs, New York.The conference provides an unparalleled platform for semiconductor professionals to network and learn the latest information in the practical application of advanced manufacturing strategies and methodologies. ASMC 2021 will be co-chaired by Ishtiaq Ahsan, Ph.D. of IBM Research and Alexa Greer of KLA.We’re looking for presentations in topic areas including the following: Advanced Metrology Advanced Equipment Processes and Materials Contamination Free Manufacturing Big Data Management and Mining Defect Inspection and Reduction Equipment Optimization Factory Automation Industrial Engineering Smart Manufacturing Yield Methodologies Click here to submit an abstract for a technical presentation. Provide an extended abstract of no more than two pages (max. of 1000 words, MS Word or PDF) with supporting data, charts, figures embedded in the last page. See author kit for details. Summarize the topic and theme in as much detail as allowed by the word count limitation. Include title, author(s), company affiliation(s), contact information, topic and five key words describing the work. The final technical manuscript must show a complete set of data to support initial abstract. Here are key deadlines and dates for industry experts to keep in mind: Abstracts Due: October 30, 2020 Author Notification: December 15, 2020 Manuscripts Due: February 9, 2021 Final Manuscripts Due: April 6, 2021 Presentations Due: April 20, 2021 Conference Dates: May 3-6, 2021 ASMC 2021 could be held as a virtual event depending on progress in containing COVID-19. Whether the event is on-site or virtual, all abstracts accepted for presentation will be published by IEEE. Speakers should be prepared to present live or online.Speakers also may be invited to publish their papers in a special section of ASMC 2021, which will be featured in IEEE Transactions on Semiconductor Manufacturing. All technical presentations will be considered for the ASMC Best Paper Award sponsored by Entegris. Students presenting an oral paper or poster will be considered for the ASMC Best Student Paper Award sponsored by GLOBALFOUNDRIES.For a complete overview of topics and other information, please visit the ASMC 2021 Call for Papers web page.Margaret Kindling is senior manager of Programs for SEMI Americas.