Industry 4.0

Automotive original equipment manufacturers (OEMs) and their direct suppliers of parts and systems share a vision: Next-generation vehicles will be more electric, autonomous and connected. At a market size of more than $1 trillion, automotive is steadily becoming a high-tech market as cars morph into advanced technology platforms with partially or fully autonomous features. Call them semiconductors on wheels. Big players such as Google and many carmakers are investing heavily in chip advances to help drive increases in silicon content in automobiles.At SEMICON Europa, Pierrick Boulay, Solid State Lighting and Lighting Systems analyst at Yole Développement, will provide a market update on autonomous automobile trends including the state of sensors, radars, cameras and LiDARs as the industry works to increase the level of autonomy and electrification.Autonomous vehicle design can only thrive with the development of an industry standard for chip and device traceability across the supply chain. The importance of chip traceability to the automotive industry is reflected in its central role in driving a chip traceability standard.According to Heidi Hoffman, senior director of technology communities marketing at SEMI, “chip traceability is one of the next big things for the technology industry. The benefits are enormous, and the upsides – including yield enhancements, counterfeiting safeguards, and support for new applications – are plentiful. But the implementation challenges of chip traceability are also big and will require considerable effort to overcome. The biggest hurdle of all? We need to transcend industry fears by demonstrating that we can secure IP when it is shared across the hardware supply chain.” The Importance of Standards, Data Collection and Collaboration Across the Supply ChainThe automotive industry has long embraced tracing the sources of defects. Now, as the automotive and semiconductor supply chains increasingly overlap, traceability has taken on greater importance in the semiconductor industry. SEMI committees, task forces and events such as the Smart Transportation Forum at SEMICON Europa are ideal platforms for collaborating to develop new standards and best practices for the automotive industry.Earlier this year, German luxury automobile maker Audi AG became the first automotive original equipment manufacturer (OEM) to join SEMI as member, strengthening alignment across automotive supply-chain segments. At SEMICON Europa, the SMART Transportation Forum and Pavilion, staged by the SEMI Global Automotive Advisory Council (GAAC) and bolstered by the Electronic System Design Alliance, a SEMI Strategic Association Partner, will gather key stakeholders across the automotive value chain, from design and semiconductor equipment to materials and carmakers, to explore innovation opportunities in automotive electronics. SEMI Global Automotive Advisory Council (GAAC) “If the industry wants to reach the goal of zero defects, a new collaborative approach is necessary,” observed Antoine Amade, senior regional director EMEA at Entegris. At SEMICON Europa, Amade will present new ways to collaborate in reducing chip defectivity and meet other challenges in the automotive industry.More than half of semiconductor failures on the automotive assembly line today (so-called 0km failures) are traced to semiconductor fab defectivity. “The increasing semiconductor content in automobiles – driven by growth in ADAS, electrification and autonomy – has put a growing focus on the quality and reliability of these devices and their implications for consumer safety and satisfaction,” said Oreste Donzella, senior vice president and CMO at KLA.The smart manufacturing (Industry 4.0) revolution is already spurring higher performance and great efficiencies throughout the supply chain and will also be crucial to driving innovation in automotive. Smart manufacturing makes possible significant improvements in factory key performance indicators (KPI) for cycle time, on-time delivery, overall equipment effectiveness, cost and product quality.“These KPI gains are key to meeting quality levels the automotive industry must reach to support the deployment of autonomous driving vehicles,” said John R. Behnke, general manager of Final Phase Systems at INFICON. In his talk at SEMICON Europa, Behnke will provide an overview of existing, in-progress, and future smart manufacturing solutions for the semiconductor industry and their impact on the automotive supply chain. The SMART Transportation Forum, 13 November, 2019 (9:30-15:30 at ICM Munich, room 14c) at SEMICON Europa is the premier platform for key stakeholders to connect, collaborate and innovate across the automotive value chain. Automotive and semiconductor industry experts will offer insights into trends in design, semiconductor equipment and materials, and automotive innovation and the roadmap to 2030. The SMART Transportation Forum will also showcase innovations in imaging, sensing, artificial intelligence (AI), smart manufacturing and L5 mobility.Other SEMICON Europa highlights: Advanced Packaging Conference: Packaging and Test Challenges Towards High Reliability (12-13 November 2019) 23rd Fab Management Forum: Game Changers for Semiconductor Operations(11-12 November 2019) Strategic Materials Conference: Strategic Materials Enabling Industry Roadmaps(12-13 November 2019) SEMICON Europa registration is open for visitors and exhibitors. For more details, please visit the SEMICON Europa website and connect with SEMI Europe on Twitter or LinkedIn @SEMIEurope (use #SEMICONEuropa).Learn more about the SEMI chip traceability standard – SEMI T23 - Specification for Single Device Traceability for the Supply Chain – and SEMI Technology Communities.Serena Brischetto is a marketing and communications manager at SEMI Europe.

Semiconductor fabs have been getting smarter and smarter over the past 30 years. It’s a natural evolution – the direct outcome of numerous continuous-improvement efforts. The really important difference on the road to smarter fabs, the one change that’s enabling the Industry 4.0 revolution, is the concept of a cyber-physical system or digital twin. If you don’t have a thorough, detailed, high-fidelity digital twin of your entire fab operation, then you cannot have “Smart Manufacturing.” That’s really the definition of a smart site. A digital twin is simply a requirement for all smart factories of the future. One caveat: No matter what you build today from a smart perspective, your digital twin’s fidelity will improve over the next 20 years. A factory’s digital twin has two facets: the operational aspect and the yield aspect. Each of these two facets places different requirements on a database including the types of data, the frequency of data generated, the retention of data, and even the AI/ML techniques used to analyze the data. A combination of these data requirements are needed to create a digital twin – the virtual representation of your entire factory operation, whether it’s on the wafer-fab front end or the assembly and test back end. What’s most important here is that facility-wide data sets and databases must be able to communicate with each other using refined summary statistics to create a practical digital twin. For example, a lot of information is collected on the yield side to feed the deep-learning models needed to manage processes. However, the factory scheduler, driven largely by the smart operational database, needs only summary statistics from the yield database to be able to act in the next moment or over the next 24 hours. Figure 1 illustrates the needs of and the interaction between a smart operational and a yield database. Figure 1: The Operational and Yield databases in a Smart Factory need to exchange summary statistics. Today, we find that although these databases generally speak to each other in smart factories, they’re still not sufficiently connected to permit the use and analysis of data needed to realize the full potential of a smart factory. That level of interconnectedness is still in the future. Some solution providers have created what is essentially a “smart learning warehouse” (“database” has become too limited a term here). This warehouse collects, analyzes and learns from the extensive amount of information that a fab generates. Game-changing, more holistic applications become possible when this information can be combined in new and informative ways. As it turns out, a data source is just a data source, but users in different factory areas need to extract different information from these common data sources. They need different applications and portals – in other words “views” – that are adapted and adjusted for each area’s needs. Aren’t we smart enough? Some people think that 300mm fabs are already smart. That’s true. They are. But, they could be a lot smarter. No 300mm fab in use today has attained the full, utopian vision of what a smart factory can deliver over the next 10 years. When you finally integrate all of the disparate databases in a fab – when you’re able to use all of those different data sources as one common data source – that’s when your Smart Factory will have the ability to self-optimize its future actions and react quickly to real-time events. The largest semiconductor manufacturers tend to develop these smart factory applications on their own. The remaining semiconductor fabs need to work together with other fabs and their solution providers to develop these smart factory applications. Why now? Why is everyone talking about “Smart” now? It’s because the semiconductor industry has helped to create all of the enabling technology: the compute power, the networking and networking standards, and even the industry’s maturation into a multi-tiered organization of solution providers. We’ve reached the point where we can collect data from a widespread sensor network along with tool-health data and we can then warehouse this data so that it can be applied to more intelligent decision-making. While there may be one or two sensors on a tool today, in the future there will be many such sensors connected over an IoT network or networks that provide mountains of data to the warehouse. All of this data will feed into the digital-twin version of the fab. One of the biggest changes on the horizon made possible by all of this accessible data is advanced scheduling. Despite all of the automation advancements made over the past 25 years, including robotic handling, it’s still hard to decide “where, what, and when?” for every single lot in the factory. Today, no factory in the world is more complex than a semiconductor fab. Optimizing a semiconductor manufacturing process is the most complex manufacturing-optimization task in the world. Do it for ROI ROI is the chief reason for having a digital twin. Once you can make a truly smart, holistic schedule of the fabs operations — not a dispatch or rule-based dispatch list — then you can create an operationally smart factory. Rule-based dispatching systems primarily focus on tools and tool-centric views. Although they incorporate knowledge from current WIP and tool conditions to make decisions better than simple dispatch systems, smart factories are not just about tools and the current WIP at them. Smart factories use the status of every tool and lot in the factory to make fab-centric optimizations instead of tool-specific optimizations. Once you have a digital twin, you’re optimizing for global functions such as line linearity and on-time delivery. These functions are not just about the moment. The transition to a smart factory thus represents a huge philosophical change. When you know exactly what’s going to happen in a factory over the next 12 hours for every single lot, every single wafer carrier, and every single entrance port of every tool in the factory, then you suddenly have control over the factory’s idle time. You know when you can optimally perform PM (preventive maintenance). You know how to best redirect material or labor resources to maximize output. You can create a smart schedule for every maintenance person in the factory that comprehends each person’s skill set and tool downtime so that there’s no negative impact on the factory’s productivity. You can only do all of this when you know the future. Figure 2 illustrates the opportunity. Imagine that a factory contains 1,500 tools. Use of these tools is scheduled for the next twelve hours. The information depicted in Figure 2 encompasses process changes from one chemistry to another, implant changes, reticle changes, and the status of every single consumable for all 1,500 tools. The white spaces that appear between processes in Figure 2 represent opportunities to intelligently schedule events such as maintenance to maximize factory productivity. Figure 2: Smart scheduling permits factory-wide optimization to maximize productivity. Once you have a schedule, you need to translate that schedule into actions or movement. It’s not easy to do this and most material-control systems today make overly simplistic decisions based on modeled assumptions and typical cases rather than the actual time each lot needs to be at a precise location, which can only come from a schedule. Once the data from all of the tools is connected, a smart scheduling system can use the digital twin to make far better process decisions. The larger the factory (or more complex the factory), the more important it is to make smarter decisions. Note: SEMI has a Smart Manufacturing Technology Community. For more information or to get involved, click here. If you would like to discuss Smart Manufacturing more with John directly, he can be contacted at [email protected]. John Behnke is general manager of the Final Phase Systems product line at INFICON.

Part 2 of this two-part piece 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. Part 1 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. So what does this observation (i.e. the field of medicine, in what seems at first glance 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) mean for semiconductor manufacturing? We think it means we need to apply the same level of intense focus that we already devote to instrumented data collection and analytics in the fab to something more: we need to better capture the vast expertise of our engineering and operational talent in semiconductor manufacturing. We think we need to record what the subject matter experts (SMEs) in the fab see, hear, and think as they investigate yield excursions or machine-down problems. We need to effectively combine product, process, equipment and component subject matter expertise / subject matter experts (SME) with big data analytics to more effectively solve manufacturing problems, be they killer or be they chronic. And we must provide structured methods for incorporating inputs from and active participation of SMEs throughout the data analysis lifecycle, from collection and aggregation, through filtering, feature extraction, analysis and optimization. Some of the challenge will be in just how do we make it easy to gather information from SMEs in real time, while standing in front of equipment in the fab. Internet of Things (Iot) devices are emerging to capture and label images and sounds to enable machine learning algorithms to recognize and help diagnose manufacturing problems based on sight and sound, complementing the instrumented data. But we also need to record the thought processes our human SMEs go through in those investigations – perhaps by the SMEs talking to a smart AI-based conversational assistant who helps make “rounds.” Doing contextual analysis on this added data, combined with the instrumented data, will create the equation Human + Machine = AI (Awesome Insight). Sounds reasonable, right? We think artificial intelligence becomes too artificial if you leave the human out of the equation. AI should be augmented intelligence, where we take the expertise and creativity of the human, and combine it with the rapid computational capabilities of the computer, in order to put problem identification and solutions on steroids. But with the already huge advancements to date in data analytics, cloud, and the emergence of AI, why do improvements in quality, machine utilization, and the implementation of predictive analytics in semiconductor manufacturing seem to be creeping along incrementally, and not appearing as dramatic, step-function improvements? Call it Smart Manufacturing, call it Connected Enterprise, call it Advanced Manufacturing, or Analytics, or Cloud, or the Digital Twin … there are no shortages of terms, philosophies, and technologies available, but why aren’t we seeing their rapid adoption? It could be it’s the downside that comes with needing people. “Good business leaders create a vision, articulate the vision, passionately own the vision, and relentlessly drive it to completion.” Jack Welch. We see from other industries that smart manufacturing conversations originating with the executives of a company thinking to implement smart manufacturing programs lead to vision; however, we also see from other industries, and from our own, that realizing this vision has often been a challenge. Why is that? One reason may be that the people who are personally vested in solutions they implemented in the past, as well as those who follow a pattern of ‘how we’ve always done things’, create, inadvertently or not, persistent internal barriers hindering innovative action. Another may be that engagements with the working engineers and managers charged to be smart manufacturing implementers leads to the pursuit of low-hanging fruit, and cautious investments, that often utilize solutions that ultimately cannot scale and integrate. Not to mention the disadvantage of dealing with the legacy equipment, the legacy networks, the traditional thinking, and the lack of consistency in metrics adding to the confusion. Addressing all these barriers requires an alignment in strategy and execution, along with a plan to support the overall vision, often across the entire enterprise, which is no small matter. And then there are the standards. Having and adhering to standards in control solutions, networks, and data becomes critical in achieving real benefits from smart manufacturing. And data security. One of the other big impediments in the smart manufacturing transformation is data and IP security, another key concern (maybe the most significant) preventing us from moving forward more quickly (e.g. to cloud-based solutions) in our industry. More about that in a follow-up. Achieving synergy across all of manufacturing, from connecting equipment horizontally, through the production system (machines processes), and vertically, through enterprise systems and across production facilities, can only occur if we build standards, security, infrastructure, and human engagement throughout our ecosystem and supply chain. In simple form, the steps to do so include connecting assets, collecting and contextualizing data, and then driving business transformation with actionable insights gained from the data. With impact on every function, and every person, in the enterprise, from equipment operators in the fab through the C-Suite in HQ. Maintenance, Engineering, R D, Operations, Scheduling, IT, Procurement, Finance, HR all contribute, collaborate and benefit. Regardless of the technology, from device level analytics to predictive maintenance and optimization, the people that reside in these disparate groups need to come together with the smart machines to create a common strategy to achieve transformational results. Aligning an enterprise’s goals with its human capital is paramount to success. Therefore, we must challenge our team members and ourselves to work outside our comfort zones, and we need to be forever aware of the need for us to grow with the technology. Smart manufacturing is not necessarily about having fewer people in the fab, but it does suggest having people in the fab, perhaps with different, or upgraded, skill sets, who are even more efficient in their roles as a result of the boost they are getting from Industry 4.0. Fortunately, we now have techniques that let us combine the best, brightest, and latest and greatest analytics with our invaluable SMEs throughout the data analysis lifecycle. We’ll not only be able to deliver higher quality semiconductor manufacturing solutions all in all, but we’ll also be providing methods to more easily distribute, scale, maintain, and continually refine those hard-earned solutions. We expect that subject matter experts will continue to put the “smart” in machine-based smart manufacturing today, and for the foreseeable future. SME contributions are not an option, but, rather, an imperative for ensuring a semiconductor manufacturer’s sustained prosperity, much less its survival. 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.

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.