Rockwell Automation

Last year the industry posted another remarkable double-digit revenue growth year. IC shipments eclipsed one trillion units for the first time and continued to enable an ever-expanding array of silicon intensive-applications.2018 was also a pivotal year of transformation at SEMI. Setting our sights firmly on building more value for SEMI members, we doubled down on priorities I established this time last year. We advocated intensively on global trade policies, industry talent needs, and critical environment, health and safety (EHS) concerns. To underpin our efforts around talent, we took the bold step to reinvigorate the industry’s identity with a dynamic image campaign. Above all, we targeted critical industry-wide issues to help us realize the ambition of becoming a trillion-dollar industry in the next decade. Workforce DevelopmentRedefining our approach to talent development in 2018 was and remains a top priority. A diverse, highly skilled workforce is crucial to the industry’s ability to innovate. Last year we ramped up a number of SEMI High Tech U (HTU) programs to inspire young people and attract them to careers in high-tech manufacturing. To date, more than 130,000 students have been touched by HTU – through student or teacher programs.Over the past year, we designed a new university outreach program and established partnerships with 100 institutions. We established Workforce Pavilions at SEMICON events in Southeast Asia, the U.S., Taiwan, Europe and Japan for students to explore career opportunities and meet with recruiters. We thrilled at seeing sponsors hire young talent at SEMI events. This year, all SEMICONs worldwide will feature Workforce Pavilions.SEMI also formalized its commitment to Diversity and Inclusion (D I) with the establishment of a D I council to shape new programs including the recently launched Spotlight on SEMI Women. To localize and fully optimize our D I programs, we established regional workforce councils in every region we serve. We unveiled the SEMI Mentoring Program to support students and young professionals on this journey by facilitating one-on-one mentoring relationships with industry professionals. Hundreds of mentees have enrolled. But we still need more mentors. I urge you to join the program. During the year, SEMI also expanded its workforce staff and developed a comprehensive workforce strategy with programs that engage students as early as elementary school and inspires them through high school and college. The program provides pathways to professional careers, building a pipeline to fill the short-term and long-term talent needs of the industry. Industry Image CampaignAs we developed the comprehensive workforce development program, we knew we had to refresh the industry’s image and appeal to the next generation through contemporary media and communications channels. So we recently launched a bold, innovative campaign to raise industry awareness and attract students and recent graduates to careers in semiconductor manufacturing.Our You’re Welcome campaign is a novel, creative approach that blends entertainment, media and storytelling to excite students about the industry. The campaign went viral immediately and within weeks had more than 5.5 million social media impressions and 2.3 million video views.Trade Policy AdvocacyRising trade tensions between the U.S. and China catapulted global trade policy to the forefront of industry concerns in 2018. Since the tariffs have taken force, semiconductor companies have faced higher costs, greater uncertainty, and difficulty selling products abroad. The tariffs have forced many SEMI member companies to pause or rethink their investment strategies.SEMI quickly engaged U.S. policymakers and provided resources for SEMI members. We formed a member trade task force, staged trade compliance seminars in China, and convened meetings with over 110 U.S. congressional, agency and administration officials, and provided testimony on the importance of the free trade to the industry.SEMI continues to educate policymakers about the critical importance of free and fair trade, open markets, and respect and enforcement of IP for all players in the global electronics manufacturing supply chain. As part of this initiative, we distributed “10 Principles for the Global Semiconductor Supply Chain in Modern Trade Agreements” and encouraged their adoption in various trade negotiations. These principles outline the primary considerations for balanced trade rules that benefit SEMI members around the world, strengthen innovation and perpetuate the societal benefits of affordable microelectronics.Environment, Health and SafetyEnvironmental regulations are proliferating globally even as advanced semiconductor manufacturing technology relies increasingly on a host of new materials. With dozens of new fabs and fab line upgrades, our industry must align on best practices, sensibly respond to materials restrictions, and renew efforts toward sustainable manufacturing.That’s why the revitalization of SEMI EHS efforts became another priority in 2018. Two months ago, we hosted the inaugural EHS Summit at SEMI Headquarters. Fully, 70 EHS professionals and company executives met to form the basis for the future SEMI EHS program.The Year AheadDespite a softening in the market, compounded by Apple’s first-ever announcement of a revenue decline in 16 years, a geopolitical whirlwind on trade and an extended shutdown of much of the U.S. government, the future is bright.At SEMI’s annual Industry Strategy Symposium (ISS 2019) in Half Moon Bay, Calif. in early January, the sense of optimism was palpable. In her keynote address, Dr. Ann Kelleher, Sr. VP and General Manager, Technology and Manufacturing Group, at Intel, observed that data is powering the fourth industry revolution and the expansion of compute. With customers expecting continual improvements in applications, Kelleher highlighted the tremendous opportunity for the chip industry to meet these expectations.At ISS 2019, we announced a Memorandum of Understand between SEMI and imec. The MOU will enable us to accelerate our members’ engagement in SEMI’s Smart vertical market platforms, in particular Smart MedTech and Smart Transportation. Our partnership with imec will also allow us to boost SEMI Standards activities in non-CMOS technologies, deepen technology roadmap efforts and augment our SEMI Think Tank initiative in thought leadership at a global level.Over the course of this coming year, will we begin our global rollout of key building blocks of our comprehensive workforce development program to engage schoolchildren as young as 10 and learners all the way to veterans who return to the workforce. We are now able, with the invaluable help of our Workforce Development Council and the passionate engagement of many SEMI member companies, to offer a solution to the talent crisis in our industry.We will continue to be the leading voice for our members and the end-to-end semiconductor supply chain across Talent, Trade, Tax and Technology as we work to ensure free, fair trade that protects IP while preserving vital access to markets to grow the supply chain. Vertical Market PlatformsOur vertical market platforms are an important part of this growth. For example, in Smart MedTech, SEMI looks forward to working with the Nano-Bio Materials Consortium to advance human monitoring technology for telemedicine and digital health after winning $7 million to fund the renewed program. In Smart Transportation, we will leverage the Global Automotive Advisory Council (GAAC) we formed last year to represent the full automotive supply chain and the Smart Transportation and Smart Automotive forums featured at all our SEMICON events to enable the industry to identify and seize opportunities in autonomous driving. At ISS 2019, Sujeet Chand of Rockwell Automation noted that “digitization will grow faster in the next 10 years than it did in the past 50,” a trend calling for semiconductor fab architectures that transform data into business value. We will continue to bring the industry together at our Smart Manufacturing venues to help uncover ways to deploy deep learning, edge computing and other Smart technologies to deliver this value and meet the challenges of automation as artificial intelligence’s (AI) sprawling influence reshapes industries including manufacturing.I am filled with optimism and thrilled about the opportunities I see on the horizon for our members as we build on our 2018 accomplishments to enable your prosperity in 2019 and beyond. My heartfelt thanks to all of you for your participation in our programs and events.I look forward to another successful year as we connect, collaborate and innovate together!Ajit Manocha is president and CEO of SEMI.

This year, SEMI ISS covered it all – from a high-level semiconductor market and global geopolitical overview down to the neuro morphic and quantum level. Here are key takeaways from the Day 1 keynote and Economic Trends and Market Perspectives presentations.In the opening keynote, Anne Kelleher from Intel pointed to the huge growth of data, with fabs collecting more than 5 billion sensor data points each day. The challenge, Kelleher noted, is to turn massive amounts of data into valuable information. Moore’s law is not dead. New models of computing benefit still from Moore’s law and advances in Si/CMOS technologies for conventional, deep learning, neuro morphic and quantum computing.With customers expecting continual improvements in applications, the question is whether the chip industry is moving fast enough to meet these expectations, Kelleher said. A broad supply chain, equipment and materials innovations, and attracting the “best of the best” college graduates to fuel innovation is key, she said.In the economic trends session, Nicholas Burns (ambassador ret.) from Harvard University pointed out that we will see a major shift in power. The U.S. will remain the major world power over the next 10 years, but we will see a major shift in power in the next coming decades as the gap with countries like China, Russia and India continues to narrow.Duncan Meldrum from Hilltop Economics said that we are passing the peak growth of economic cycle. He warns that a more likely outlook is that a global growth recession is developing. Although semiconductor MSI growth will see a noticeable slowdown in 2019 and 2020, the semiconductor industry is still healthy over the longer term.Bob Johnson from Gartner sees demand shifting from consumer to commercial applications with higher ROIs and budgets. AI, IoT and 5D are the major enablers. He sees structural changes in the semiconductor industry especially for memory but also for Moore’s law with increasing costs and fewer players.The DRAM markets shows volatility and NAND market may be negative in 2019 but non-memory are expected to accelerate mainly because of increasing content and some price hikes.Overall Gartner expects good long-term growth with a CAGR (2017 to 2022) of 5.1%, outpacing 2011 to 2016 CAGR of 2.6%. After a strong 2018 with 13.4% revenue, he forecasts a slower 2019 with 2.6% growth followed by a 8% growth in 2020 and negative growth rate in 2021.Andrea Lati of VLSI went “Back to fundamentals” in his presentation about the industry. VLSI sees a downside bias due to slowing global economy, tariffs, and trade wars. Future drivers are data economy, cloud, AI and automotive.As memory leads the 2019 slowdown, analog, power, logic and other sectors remain in positive territory. VLSI lowered its semiconductor equipment forecast for 2018 from 20% (Jan. 2018) to 14% (Dec. 2018) but increased its sales outlook from 8% to 15% in 2018. VLSI expects revenue to slow into the first half of 2019 but increase to over 4% in the second half of the year, resulting in total 2019 drop of 2.7%. Semiconductor equipment sales are expected to drop from 14% in 2018 to -10% in 2019.Michael Corbett of Linz Consulting, covering wafer fab materials in the years of 3D scaling, sees these as good times for the industry. His outlook for wafer fab materials is bullish based on strong MSI and because wafer fab materials suppliers are getting bigger because of M As.In the Market Perspective session, Sujeet Chand of Rockwell Automation pointed out that as more and more data is generated, the problem is how to get value of all the data collected. There is a need to create the right architecture for machine learning and AI and big data is increasingly being replaced by contextual/structured data. He expects Industry 4.0 to drive foundries to become smaller, more flexible and more productive.In the Technology and Manufacturing session, Aki Sekiguchi of TEL addressed process challenges in the age of co-optimization. The semiconductor industry continues to expand, driven by massive growth of interconnected devices, with heavy demand for processing power and storage. He expects an exponential increase of data from about 40ZB in 2018 to 50ZB in 2020 to 163 ZB in 2026.Major technologies such as DRAM, 3D NAND and logic are dealing with scaling challenges. The density of DRAM (Mb/chip) is plateauing according to 2015 to 2020 trend data, with DRAM is in need of EUV. Memory capacity demand is leading to increasing layers and higher aspect ratios that is concern for 3D NAND and mainly for plasma etch. With Logic already implementing 3D structures, it appears to be in a solid position. Buddy Nicoson of Micron talked about his 50 years in the industry and looked ahead to the next 50. The anchors – quality, cost, scale and speed – won’t change. It has been a great journey so far with unprecedented opportunities and challenges ahead of us. We are getting into a convergence (specialization, integration) and solution-based phase. We will see some inflection points in the coming years, with the best yet to come.Christian G. Dieseldorff is senior principal analyst in the Industry Research and Analysis group at SEMI in Milpitas, California.

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

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

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