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 CPUs in Summit, the world's new fastest supercomputer are built on 14nm FinFET-on-SOI technology. Yes, those IBM Power9 CPUs are fabbed by GlobalFoundries (you'll also find them in the z14, the most recent in IBM's z-series of servers – a series that's been on various iterations of SOI since its launch in 2003, btw). Summit's at the U.S. Department of Energy’s Oak Ridge National Laboratory (ORNL) in Tennessee, USA. It is now the top US supercomputer, and it's for science. The IBM-built Summit currently claims the spot in the Top500 as the world's smartest and most powerful supercomputer. “It is capable of performing 200 quadrillion calculations per second — or 200 petaflops — making it the fastest in the world,” says IBM's Dr. John E. Kelly, III, SVP, Cognitive Solutions and IBM Research. “But this system has never been just about speed. Summit is also optimized for AI in a data-intense world. We designed a whole new heterogeneous architecture that integrates the robust data analysis of powerful IBM Power CPUs with the deep learning capabilities of GPUs. The result is unparalleled performance on critical new applications.” And if that's not impressive enough for you, it's also #5 on the Green500 list for the world's most energy-efficient computers, posting Power Efficiency (GFlops/watts) of 13.889. [caption id="attachment_11940" align="alignright" width="300"] Summit supercomputer nodes: The IBM-built Summit supercomputer is the world's smartest and most powerful AI machine. It consists of 4,600 individual nodes. Each node contains two 22-core 3.07GHz IBM POWER9 CPUs, which are built on GlobalFoundries' 14nm HP FinFET-on-SOI technology, as well as six NVIDIA Telsa GPUs. (Photo Credit: ORNL).[/caption] As GF noted when they announced the technology in the fall of 2017 (read the GF press release here), their 14HP is the industry’s only technology to integrate a FinFET transistor architecture on SOI. Featuring a 17-layer metal stack and more than eight billion transistors per chip, the technology leverages embedded DRAM and other innovative features to deliver higher performance, reduced energy, and better area scaling over previous generations to address a wide range of deep computing workloads. These technologies have long, deep histories (and were developed in close collaboration with SOI wafer leader Soitec). Here at ASN we have a fabulous archive of pieces contributed by IBM explaining the genesis of the technology – they're great reads and still entirely pertinent: FinFET on SOI: Potential Becomes Reality (by T.B. (Terry) Hook et al, 2013) – this presents the key technical data. IBM: Why Fin-on-Oxide (FOx/SOI) Is Well-Positioned to Deliver Optimal FinFET Value (by Terry Hook, 2012) – this great piece busts myths and clearly explains why FinFETs on SOI deliver top performance. IBM: FinFET Isolation Considerations and Ramifications – Bulk vs. SOI (by Terry Hook, 2013) – explains why and how SOI increases operating voltage range, simplifies processing, reduces variation, lowers soft error rate, and enables higher circuit density. Embedded Memories in SOI – (by Subramanian S. Iyer, 2006) explains the importance of SOI in the memory part of the chip design equation. [caption id="attachment_11939" align="alignleft" width="300"] The IBM POWER9 processor delivers unprecedented speeds for deep learning and AI workloads. IBM Engineer, Stefanie Chiras tests the IBM Power System server in Austin, Texas. (Photo Credit: Jack Plunkett/Feature Photo Service for IBM).[/caption] As ORNL noted in its press release (you can read it here), the first projects will apply machine learning and AI to astrophysics, materials science, cancer research and systems biology. BTW, Summit also has a slightly smaller sister machine called Sierra, going in at the Lawrence Livermore National Laboratory (part of the Department of Energy's National Nuclear Security Administration). With 4,320 nodes (each also containing two 22-core 3.07GHz IBM POWER9 CPUs, which are built on GlobalFoundries' 14nm HP FinFET-on-SOI technology, but just four NVIDIA Telsa GPUs), Sierra's claimed the #3 spot on the June 2018 Top500 list of the world's most powerful supercomputers. And the Power 9 is now finding it's way into major data centers – like Google's (read about that here). There have been some good pieces in the press about it, including in Forbes and The Motley Fool. So yes, clearly there are exciting markets for FinFETs on SOI!

As artificial intelligence’s (AI) sprawling influence reshapes industries from logistics and healthcare to automotive and manufacturing, Taiwan is poised to leverage its cutting-edge capabilities and rich history in semiconductor manufacturing to stake out a leadership position in AI. Taiwan’s semiconductor manufacturing industry accounts for a major share of the region’s GDP and, with its manufacturing prowess, the region is fertile ground for using AI to optimize and even revolutionize chip manufacturing. In an AI and Semiconductor Smart Manufacturing Forum recently hosted by SEMI Taiwan, experts from Micronix, Advantech, Nvidia and the Ministry of Science and Technology of Taiwan (MOST) shared their insights on how deep learning, data analytics and edge computing will shape the future of semiconductor manufacturing. Here are four key takeaways.1. Monitor, Forecast, and PreventToday, tier 1 foundries use AI tools to combine equipment know-how and manufacturing statistics in managing massive Fault Detection (FD) data, much in the way that a car’s tire-pressure monitoring system helps maintain safe inflation levels and prevent accidents. For example, AI enables the real-time collection and monitoring of massive amounts of processing data, then alerts system administrators of any hardware failures or other manufacturing abnormalities.AI also makes it possible to adopt Run-to-Run (R2R) control to automate manufacturing process adjustments and corrections by providing feedback that can drive higher processing efficiency. In addition, virtual metrology replaces manual sampling inspection for comprehensive quality control, enabling foundries to improve yields, reduce costs, and strengthen their competitive advantage.2. Beyond Automation: Edge Computing The evolution of IoT is giving rise to a paradigm shift in the industry as the recognition grows that smart factories must go beyond automation to focus also on intelligence. All information – from equipment status and manufacturing process statistics to on-site environmental data – needs to be collected through sensors. In highly time-critical scenarios, returning all sensor data to the cloud for processing is time-consuming and impracticable. This is where edge computing’s real-time features and lower cost than cloud computing come into play.How does edge computing work in a smart factory? First, a rich trove of data from various devices is collected and integrated via Manufacturing Execution Systems (MES). Software analysis then produces a real-time factory production status before production data is visualized through a combination of system platforms and human-machine interfaces. In the end, the data is analyzed realtime in the cloud so failures can be predicted and prevented to help increase capacity and reduce costs. The approach is even capable of Bill of Materials (BOM) predictions, allowing better collaboration between upstream and downstream suppliers.3. Deep Learning Accelerates AI Deep learning enables autonomous driving, intelligent voice assistance and many other AI breakthroughs. The heart of deep learning is its ability to automatically process and learn data in various formats such as images, video and text with no human domain knowledge. This increases predictive accuracy and efficiency in processing massive amounts of data. Deep learning also enhances the efficiency of human-machine collaboration.4. Taiwan’s Competitive Niche: Industry 3.5Industry 4.0 is not just about improving production management. It also focuses on integrating supply chains, even among competitive companies. For Industry 4.0 to thrive, rival companies must grow together. The first and third industrial revolutions centered on disruptive technologies like steam engines, transistors and digital, while the second and fourth revolutions homed in on competition among various business models, platforms and industry ecosystems.While Taiwan’s strengths include innovation, short time-to-market, low manufacturing costs, and high supply chain management efficiency, the region still lags advanced countries in basic industry and research capabilities. Squeezed by Chinese supply chains and high-end manufacturers in advanced countries, Taiwan should start by carving out an Industry 3.5 niche for the island’s manufacturers. SEMI will continue to facilitate cross-industry connection, collaboration and innovation to help manufacturers seeking higher production efficiency and lower costs incorporate AI as a core competitive advantage. At SEMICON Taiwan 2018, SEMI will unveil its Smart Manufacturing Journey, an exhibition that gathers leading AI companies such as ABB, Advantech, Nvidia, Sony and UPS to demonstrate a comprehensive roadmap for smart manufacturing technologies and applications. For more information, please visit the SEMICON Taiwan website.Emmy Yi is a marketing specialist at SEMI Taiwan.

Part 1 of this two-part piece explores best-practice perspectives on collecting and utilizing smart data in industries outside semiconductor manufacturing, one of the important takeaways from the Smart Manufacturing panel discussion at SEMI ASMC 2018. Part 2 examines the potential benefits to be realized by pairing human Subject Matter Experts with smart silicon assistants, and what these new arrangements mean for semiconductor device manufacturing. The spacecraft Discovery and its HAL 9000 computer system had a digital twin. Did you know? Stanley Kubrick’s seminal film “2001: A Space Odyssey” had its theatrical release 50 years ago this April. “2001” isn’t just a great science fiction film. Rather, it’s a great work of cinema overall, across any category. (The American Film Institute lists “2001” as #15 in the AFI Top 100; a bit below “Vertigo,” a bit above “It’s A Wonderful Life.”) It’s a film so distinguished and so prescient that its lessons can inform our thinking about smart manufacturing, Industry 4.0, and artificial intelligence (AI) today. Not to give too much away, but the earth-bound digital twin of Discovery / HAL identifies a diagnostic error the onboard, Jupiter-bound HAL 9000 has made, things go awry from there, and one of the mission pilots, astronaut Dave Bowman, is forced to intervene. At the recent SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2018, on 02 May 2018 in Saratoga Springs, NY, five diverse panelists representing capital equipment, IDMs, academia, the semiconductor supply chain, and smart manufacturing best practices outside the semiconductor industry engaged in a lively discussion with the ASMC attendees. They explored where “smart” is in our industry today, where it’s headed, and what that’s going to mean for us -- the professionals who have brought semiconductor manufacturing to the current state of smart, and are looking to implement an ever-smarter tomorrow. Not to give too much away, but the panelists and audience agreed that there’s nothing artificial about pairing human intelligence with machine-based smart manufacturing. Implementing an ever-smarter tomorrow in semiconductor manufacturing requires smart people just as much as it requires smart machines. Moving towards “smart” means understanding how to derive useful information and actionable intelligence from the ever-increasing pool of big data created during semiconductor manufacturing. Modern manufacturing sites are extensively instrumented today, and create massive amounts of data to consume, decipher, base decisions upon, or discard. As we dig into this problem we realize that equipment and processes in our industry are both obviously complex, but, also, subtly complex. Semiconductor manufacturing tools easily contain 100s to 1000s of components working together to produce nanometer scale, angstrom scale, or even atomic scale features using complex chemical, physical, and plasma processes. There is a plethora of potential failure points and modes, and despite our best efforts to collect more data, many processes continue to be only poorly observable. On top of that, semiconductor fabrication processes are always drifting, and the operational context is continually changing as we change product mix, process maintenance swap-out kit components, and operating conditions and recipes. Sounds like … hospitals, and healthcare? When you see your doctor, she will collect and look at your instrumented data – blood work, blood pressure, weight, and other quantifiable factors. But, typically, your doctor won’t draw a conclusion based on that analysis alone. Rather, your doctor will sit with you, ask probing questions, and record what she asked, your responses, and what she saw, what she heard, and what she thought. Then she’ll build a hypothesis, combining the “anecdotal” data with the instrumented data, and derive from that data set both a likely diagnosis and an effective course of action. In this case, beyond the instrumented data, two humans, and their natural language input, are part of the equation: the patient, with his observations and thoughts, as well as the doctor, with hers. And it’s been a formula for success. Healthcare has made huge, step-function improvements across a spectrum of deadly diseases, as well as with less-deadly chronic afflictions, by harvesting this complex input, committing the proven disease presentation – disease diagnosis – and disease treatment models to medicine’s collective memory, and then teaching the next generation of healthcare providers both the general methods and the standard protocols essential to maintaining good health and successful outcomes. Maybe, in medicine, what seems a big data environment is really just clusters and clusters of loose small data connected by the collective neural network of highly trained doctors and their colleagues. Nancy Greco (IBM Watson), Dave Mayewski (Rockwell Automation), James Moyne (University of Michigan / Applied Materials), and Paul Werbaneth (Intevac, Inc.), along with Julie Jacob (Ernst Young), and Carson Henry (Micron Technology), were members of the SEMI ASMC 2018 panel discussing Industry 4.0 and the Future of Commercial Semiconductor Device Manufacturing. All opinions here are purely our own. Please contact Paul Werbaneth via email at [email protected]. The SEMICON West (July 9-11, 2018, in San Francisco) Smart Manufacturing Pavilion features working production equipment on the floor and three full days of speakers providing insights on building the infrastructure needed to enable AI. Equipment from Bosch Rexroth, Cimetrix, Rudolph Technologies, INFICON, Final Phase Systems, OMRON, DISCO and Edwards Vacuum will showcase cutting-edge smart manufacturing technologies. For information on the SEMI Smart Manufacturing initiative and how to get involved, please click here.

Chip testing is becoming smarter and more complex, creating growing requirements to stream data in real time and ensure it is ready to use for analysis, regardless of the vendor source. Adaptive testing using machine learning to predict die performance in a downstream test can reduce the number of cycles by as much as 40 per cent without compromising test performance, notes Dan Sebban, VP of data analysis, OptimalPlus, who’ll speak on machine learning challenges at SEMICON West’s Test Vision 2020 program. “As devices and their test requirements grow in complexity, the motivation for automating adaptive test greatly increases,” he states, adding that characteristics such as die location on the wafer, defects on neighboring die, condition of the tester, and test values near the specification limits can help predict which die are likely to be good.“The big issue we see is that while everyone likes the idea of machine learning, it remains a black box model, with little visibility into why it makes the decisions it does,” adds Sebban. In addition, a suitable infrastructure to run, deploy and assess a machine learning model in real time is required. “There is still some hesitation to adopt machine learning. It’s a big change of mindset. While building the confidence to use machine learning will take time and experience, using the technology to automate big data analysis with the relevant infrastructure may be our best alternative to reduce test cost.” Systems test and parts-per-billion quality become the ruleSystems test will continue to become more prominent and more complex as chips and packages shrink, affirms Stacy Ajouri, Texas Instruments system integration engineer and Test Vision 2020 event chair. “Even IC makers now need to start doing more systems test.” And as more ICs are used in automotive applications, the distinction between consumer and automotive requirements is blurring, driving demand in other markets for higher precision test with parts-per-billion defectivity requirements.“Intelligent test gets increasingly challenging as devices become more complex and as testing moves from distinguishing good from bad devices to figuring out how to repair and trim marginal devices to make them good,” adds Derek Floyd, Advantest director of business development, this year’s program chair. “We’re highlighting efforts to create the infrastructure the industry needs to manage big data for machine learning with test platforms from different vendors,” says Ajouri, citing work on new standards for streaming data from the testers and labeling critical steps in consistent language to simplify the use of data from different platforms in real time. “I have 10 platforms from multiple vendors, and I need them to mean exactly the same thing by ‘lot’ so I don’t have to sort it out before I can use the data,” she says.Are devices becoming too complicated to test at the required price point?Can testing be economical with up to a million die per wafer, 50 data points per die, a requirement for parts-per-billion accuracy, and the need to identify parts that test good now but that might fail in the future? Organizers of the event invite chipmakers and test suppliers to debate the issue. “The speed of innovation in the semiconductor industry challenges test to keep pace,” notes Floyd. “The product we’re testing is always ahead of the product we have to test it with.”The two-day event features sessions on automotive test; big data and machine learning for adaptive test; handling and interface issues such as over-the-air testing; and a general session covering memory and RF test.Paula Doe, SEMI

The MCU at the heart of Sony's new smart-sensing SPRESENSE™ for IoT is built on FD-SOI. Why? Low operating voltage and low power consumption, of course! Sony's got two cool new products going on sale in July 2018: the SPRESENCE main and extension boards for IoT applications, equipped with a smart-sensing processor (read the full press release here). A CXD5602PWBCAM1 camera board for sensing cameras will go on sale in August. All were on display at the SF Maker Fair '18, where they were an instant hit. [caption id="attachment_11931" align="alignright" width="300"] Here are the main features of Sony's CXD5602 MCU for IoT, which is built on FD-SOI. (Courtesy: Sony Semiconductor Solutions)[/caption] The main board (it's open source, btw) will run about US$50. You'll find the specs and main features here. Spresense is powered by Sony's FDSOI-based CXD5602 MCU (ARM Cortex-M4F × 6 cores), with a clock speed up to 156 MHz. The main board utilizes a multi-CPU structure equipped with Sony's state-of-the-art GNSS (Global Navigation Satellite System – which they talked about at the most recent SOI Symposiums in SF and Tokyo) receiver. A variety of systems for diverse applications, including drones, smart speakers, sensing cameras and other IoT devices, can be built by combining these boards and developing the relevant applications. The new board can be used to control a drone, for example, using GPS positioning technology and a high-performance processor, voice-controlled smart speakers, low-power consumption sensing cameras and other IoT devices, etc. It can also be combined with various sensors for use in systems that detect errors in production lines on the factory floor.

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

Device manufacturers continue to invest. Spending in cloud data center (compute, networking and storage), automotive (content per car increases), industrial (on content, factory automation, and positive macro trends), and consumer (gaming) end-markets is particularly strong. We see capital expenditure growth in 2018 and early indications pointing to sustainable spending into 2019. We also expect 14 percent increase (YoY) for fab equipment spending in 2018, up from the February forecast of 9 percent, and expect 9 percent increase in 2019, adjusted from the February forecast of 5 percent. 92 future facilities/lines with various probabilities are scheduled to start production in 2018 or later. Fab investment is just one indicator of how growing demand in areas such as from Artificial Intelligence (AI), cloud/data storage, automotive and Internet of Things (IoT) is driving unprecedented spending in the semiconductor industry. Below are a few highlights* of recent SEMI FabView insights. Details of each project can be found in FabView online 24/7 or World Fab Forecast report (Excel format). Infineon’s new 300mm Fab in Austria - Infineon is planning a new 300mm thin wafer Fab for Power Devices in Villach, Austria. Rumors on Toshiba’s new Fab plans - More 3D NAND fabs in the future at Toshiba are feasible. The timing will depend on market conditions, and our forecast will adjust accordingly. Vanguard's possible 300mm foundry fab - Vanguard's management said it might buy or build a 300mm fab in the near future as all 200mm fabs are essentially full. Powerchip plans to build new memory fab in Taiwan - Powerchip is investing more in expansions since Memory pricing is holding up. Rohm announced to build a new SiC fab in Fukuoka Japan - Rohm announced its plans to build a new SiC fab. Micron is building a new fab in Singapore - Micron broke ground in a ceremony for a new fab in Singapore on April 4, 2018. Bosch had groundbreaking ceremony of their 300mm fab in Dresden end April 2018 - Investment of 1 billion Euro. This is the biggest single investment in Bosch’s 130-year history. SEMI FabView, a mobile-friendly, interactive version of SEMI’s popular World Fab Forecast, delivers on-demand fab information such as fab spending and capacity for over 1,100 facilities, including over 82 planned facilities worldwide, across a wide range of product segments including Power, GPU, Memory, Foundry, MEMS and Sensors fabs. Fab data include region, start of construction, operation, construction and equipment spending, capacity, wafer sizes, product types and geometries. SEMI FabView subscribers receive forecast model updates through SEMI’s World Fab Database. Click here for a trial to experience SEMI FabView first hand. *Actual updates provide more detail Christian G. Dieseldorff and Clark Tseng, Industry Research Statistics Group, SEMI.

The U.S. Trade Representative (USTR), based on findings from its Section 301 investigation into China's trade practices, today announced a 25 percent tariff on $34 billion in Chinese goods including many products in the semiconductor supply chain.Products such as test and inspection equipment and spare parts that enter the U.S. from China will be subject to this tariff, which enters into force on July 6, 2018. About 80 percent of the semiconductor products originally proposed remain on the final list of tariffs.USTR also has proposed tariffs on more than $16 billion worth of goods including chemicals as well as machines and spare parts that are used to manufacture semiconductor devices, wafers, flat panel displays, and masks, all of which would squarely strike the semiconductor industry. This new proposed list includes products identified by the U.S. government that have particularly benefited from Chinese industrial policies such as “Made in China 2025.” SEMI is set to voice its opposition to these tariffs with written comments and at an upcoming public hearing.Over the past month, SEMI has submitted written comments and offered testimony on the damaging impact that tariffs would have on the U.S. semiconductor industry. While SEMI strongly supports efforts to better protect valuable intellectual property, we believe that these tariffs will do nothing to address U.S. concerns over China’s trade practices. Instead, the tariffs will harm companies in the semiconductor supply chain by increasing business costs, introducing uncertainty and stifling innovation.SEMI will continue to engage with lawmakers as the $34 billion in tariffs take effect and the proposed $16 billion in duties remain under consideration. We encourage members to review this list and determine the level, if any, of impact. If you have questions or concerns, please reach out to Jay Chittooran, Public Policy Manager at SEMI, at [email protected].

Updated Global GDP ForecastThe World Bank just updated its multiyear forecast for GDP growth both globally and by country (Chart 1).It noted: “Despite recent softening, global economic growth will remain robust at 3.1 percent in 2018 before slowing gradually over the next two years, as advanced-economy growth decelerates and the recovery in major commodity-exporting emerging market and developing economies levels off.“This outlook is subject to considerable downside risks. The possibility of disorderly financial market volatility has increased, and the vulnerability of some emerging market and developing economies to such disruption has risen. Trade protectionist sentiment has also mounted, while policy uncertainty and geopolitical risks remain elevated.”www.worldbank.orgSemiconductor Growth Outlook Strong (Chart 2)The WSTS updated its world semiconductor shipment forecast. This new forecast (endorsed by SIA) projects worldwide semiconductor sales will be a record $463 billion in 2018, a 12.4 percent increase from 2017. WSTS projects year-to-year increases across all regional markets for 2018.This revised semiconductor forecast coupled with very robust global semiconductor capital equipment sales (Chart 3) paint a positive outlook for 2018.www.semiconductors.orgwww.semi.orgVery Strong End Market Growth in First Quarter (Chart 4)Based upon the combined 1Q’18 financial reports of 213 large, global OEMs, electronic equipment sales (consolidated into U.S. dollars) increased globally an estimated (and very robust) 10.6 percent in 1Q’18 vs. 1Q’17. While this world growth result is very heartening it was significantly inflated by exchange rate effects as stronger non-dollar currencies were converted into weaker dollars. Looking at world electronic equipment sales consolidated into both dollars and euros, 1Q’18 growth rates are MUCH different (Chart 5). 1Q’18 vs.1Q’17 electronic equipment sales grew 10.6 percent in dollars but declined 4.3 percent in euros! Certainly the first quarter was strong globally but the currency chosen for analysis can have a BIG effect.U.S. Supply Chain Expansion ContinuesLooking at the U.S. market (in dollars - therefore not distorted by exchange rates) domestic electronic equipment orders rose 6.7 percent in February-April 2018 versus the same three-month period in 2017. The U.S. electronic industry is doing reasonably well at present.www.census.gov/manufacturing/m3/Expect the recent exchange rate based amplification of dollar denominated global growth to taper off quickly.Keep a careful watch on the geopolitical situation.Walt Custer of Custer Consulting Group is an analyst focused on the global electronics [email protected]