Smart Manufacturing

Semiconductor, electronics and equipment manufacturers today face a number of logistics and supply chain challenges that could be overcome by systems providing a secure, tamper-resistant, single source of truth. Chief among these challenges is limited data sharing due to data security barriers among suppliers, shippers, manufacturers and test houses, an impediment to achieving optimal product quality and regulatory compliance. Additionally, inefficient and inadequate processes for tracking goods make it more difficult to isolate shipping problems, track faulty parts and verify product authenticity. Counterfeiting has become a serious problem that costs US-based semiconductor manufacturers $7.5 billion annually.How Blockchain Can Help Clear Data Sharing BottlenecksBlockchain functions could help alleviate many data sharing pain points in manufacturing. Blockchain’s distributed functionality, bundled security measures, and associated features such as smart contracts have the potential to help manufacturers quickly trace goods, manage records transparently, and automate supply chain processes and payments. No isolated blockchain platform would solve all of these problems on its own. But, when combined with other solutions and applied to particular use cases, blockchain has the potential to optimize operations and foster an environment of trust and collaboration among consortium members. Three core features of blockchain make it a valuable technology for manufacturing: Distributed and immutable system of record. With a distributed system of record in the blockchain network, there is no "central" data store controlled by one organization. The distributed ledger provides all participants with a view into the data, thus increasing transparency, data distribution timeliness, information sharing, and data access. Security also improves as there is no single central data store open to external attacks. Once data is inserted onto the chain, it cannot be easily changed. Security and Trust. Blockchain integrates best-of-breed cryptographic mechanisms to guarantee the digital identity of the network participants and secure the privacy of the data stored to enable role-based data access. It brings trust to a potentially trustless environment without the need for a centralized third party. Smart Contracts. Smart contracts are embedded business logic that can be added to a blockchain. They enable the automation of many processes and the secure handling of contracts. Blockchain Use Cases in ManufacturingIn each stage of manufacturing, blockchain could be applied in a variety of use cases to expedite processes and alleviate security issues. A few examples that merely scratch the surface of what may be possible follow.In pre-production, manufacturers may implement blockchain solutions for Collaborative Planning, Forecasting and Replenishment (CPFR). These systems monitor inventory levels, enabling suppliers to replenish supplies before they run low. The expensive, proprietary B2B networks used today could be replaced with blockchain as the common sharing protocol, using non-proprietary or public networks.Suppliers may also combine blockchain with IoT sensors on shipping containers to provide a tamper-resistant record of shipping conditions. This could be used to ensure that temperature and humidity tolerances for chemicals and equipment are not exceeded during transit from the supplier. The identity and materials in components and subcomponents of manufacturing equipment could be collected on a blockchain to verify compliance with environmental and health regulations. During production, a manufacturing process machine can be registered on a blockchain with a unique identity; its performance and maintenance history can be recorded. A maintenance service provider could then be automatically notified, via a smart contract, when a predictive maintenance alert is written, allowing repair of machines before they fail. In the distribution stage, customers could search the ledger for a product’s complete history, reducing counterfeiting and solidifying the origin of properly sourced goods. When faulty product is identified, the manufacturer may search the ledger to quickly locate the faulty supplier or bad test results and alert all receivers of the defective product.ConclusionWith blockchain, manufacturing can become a more collaborative process among suppliers, manufacturers and customers. Blockchain can help streamline the supply chain and inventory replenishment, improve tracking and regulatory compliance, and reduce counterfeiting. Augmenting blockchain with IoT enables use cases like predictive maintenance and monitoring of goods during transit. Blockchain is not yet mature and its business value still needs to be proven. However, it is poised to help manufacturers decrease costs and fraud, and provide customers with faster, more secure delivery, increased visibility, and consistency.More Resources on Blockchain and ManufacturingTibco is an active member of SEMI’s Smart Manufacturing Technology Community, which holds regular meetings on this and other topics. Join now to help shape the future of Smart Manufacturing. For more information on blockchain use cases in manufacturing, please see these resources. Read this Whitepaper: Blockchain and Manufacturing: A Match Made in the Factory Watch this Webinar: Blockchain and Manufacturing - A Match Made in the Factory Visit the TIBCO Blockchain Solutions page Mike Alperin is a TIBCO principal manufacturing industry consultant embedded in the Data Science team where he applies analytics, machine learning and big data technology to current industry problems. Prior to this he was the product manager for a leading commercial yield management application. He has worked at start-ups and global semiconductor manufacturing companies as a yield manager, device engineer, process engineer and failure analyst. Mike is based in Austin, Texas.

OSATs (outsourced assembly and test companies) currently handle the bulk of assembly and test activity for the worldwide semiconductor industry. These companies’ factories have a manual operation legacy: Decision-making is manual. Materials and WIP (work in process) movement is manual. Practically everything in these factories is done manually. In addition, OSAT factory environments typically present many physical constraints with respect to equipment layout, material carriers and storage. All of these constraints present challenges when trying to automate material handling in these factories. OSATs also operate with far smaller gross and operating profit margins than IDMs, yet the percentage of worldwide semiconductor product handled by OSATs is currently increasing from year to year while the IDM share is decreasing. The combination of increased business volume with lower margins encourages OSATs to automate their factories, but there are challenges that must be overcome. Technical challenges abound OSATs face many technical challenges when trying to automate production. First, installed legacy equipment in these factories is typically 25 to 30 years old. This older equipment was simply not designed to accommodate automated materials handlers. For example, access doors on older equipment make automated WIP delivery and pickup nearly impossible without significant modifications to the equipment. Second, these factories are not equipped with the infrastructure needed to support automation. To start with, most of this older equipment is not SECS/GEM compliant. (SECS/GEM is the semiconductor industry's standard equipment interface protocol for equipment-to-host data communications.) This capability must either be retrofitted to the existing equipment or some other means of extracting required data from the equipment – getting it from the PLCs controlling the equipment, for example – must be employed. Similarly, the WIP carriers currently in use – wafer carriers, trays, magazines, and the like – are not designed for automation. In contrast to the semiconductor wafer fab industry, it seems that almost every company in the OSAT domain has a different idea concerning what a carrier should look like. In particular, there’s no such thing as the standard 300mm FOUP (Front Opening Unified Pod), which carries wafers from one tool to the next inside of semiconductor fabs. The variations in carrier shapes, configurations, and even gripping handles in the OSAT domain thwarts progress in OSAT factory automation. How do you design a materials-handling robot with the grippers and flexibility needed to adapt to all of these different carriers? It’s a difficult question and an expensive proposition. OSAT facilities themselves are designed for human-based materials handling, not automated materials handling, simply because they were designed at a time when automation was not contemplated. As a result, the equipment in these facilities is packed very closely together (to reduce floor space costs), as shown in Figure 1. Figure 1: Equipment in a test facility is often tightly packed, which impedes the adoption of automated materials handling. It’s very difficult to add automated materials handling equipment at floor level or even at ceiling level in these OSAT factories, as is frequently done inside of a semiconductor wafer fab. You will not see AGVs (automated guided vehicles) moving around inside of legacy OSAT factories because there’s simply no room for them to move around. Tackling the challenges So, what can be done to handle these all of challenges? You must start by understanding the nature of the operations taking place inside of the factory. As stated above, most of these operations are currently performed manually. All of the decisions and the materials transport is performed by humans. There’s simply no way to transition from a fully manual operation to a fully automated operation in one jump. It’s too far a reach. A significant amount of work is needed just to reach the level where automated decision making is possible. Key systems must be added to enable this level of automation. Many companies tried and failed to automate assembly and test in OSAT facilities about 25 years ago. They failed because the required data could not be extracted from the equipment in use and, therefore, there was no data to drive good decision-making. Too many required systems were simply lacking. For example, when AGVs were added, one or two operators had to walk along with the AGV to tell it what to do. There was no benefit from the automation in this example. There was no successful path to automation at the time. Standards needed One of the major obstacles to automating assembly and test in OSAT facilities is a lack of standards for carriers, robotics, layout, and facilities. Many front-end standards exist. The SEMI-E82, SEMI-E84, and SEMI-E88 standards designed for semiconductor fab front ends might apply, but they need to be adapted to requirements for OSAT back-end facilities. In addition, OSATs have special needs that may demand new standards. This is a real opportunity for SEMI and its constituents. An architecture for full assembly and test automation involves four layers, as shown in Figure 2. Figure 2: Full automation for assembly and test involves four layers. Starting with the data layer at the top of Figure 2, a fully automated facility needs to have database systems in place that can supply all of the data needed for making smart scheduling and dispatch decisions. These databases then feed smart, automated scheduling and dispatch applications in the logic layer. The scheduling and dispatch applications then send control commands to the automated transport and materials controllers and the automated equipment handlers in the control layer. You need to start at the top of the diagram to put all of this automation in place. The automated equipment and equipment controllers need commands from the scheduling and dispatch applications, which in turn need data from the databases to make smart decisions. So it’s the data layer and the systems that feed data to this layer that constitute the starting point for the journey to full automation. A significant amount of simulation is needed to develop optimal facility workflows. These simulations are driven by data extracted from the databases. One of the frequently ignored facets of automation is the need for backup plans. For example, what is the backup plan when an AGV fails and cannot deliver material as scheduled? Simulation helps create contingency plans for such events. A case study Applied Materials has worked with assembly and test factories in deploying full automation. Towards this objective, the factories have worked on many modifications (physical and systems) to enable this automation. For example, a die-attach machine was retrofitted for automation by removing all of its equipment doors so that an AGV could load the machine and extract completed work. Additional modifications permitted the mounting of multiple magazines on the die-attach machine’s input and output to provide the buffering needed to smooth the flow of work through the machine. Finally, simple instrumentation and networking was added to the machine to aid in making WIP delivery and pickup decisions. These machine modifications addressed only the bottlenecks in this particular machine, but even these simple modifications helped to reduce the incidence of manual handling errors, such as the misalignment of magazines or trays. Modifications like these also reduce the need for human operators, which in turn reduces operating costs. Such types of incremental enhancements in automation capability have been implemented by leading-edge companies over the past few years. Conclusion Deploying full automation for assembly and test is not only feasible, it’s necessary for future profitability. OSATs must address the challenges of rising manufacturing volumes and thin margins by reducing manufacturing errors and increasing quality. (The quality requirement is increasingly driven by the automotive industry.) Trailblazing deployments have shown that it’s possible to automate these manufacturing lines successfully. While IDMs have a longer history for manufacturing automation, OSATs are now traveling along the same path due to their rising share of worldwide manufacturing volumes. On that path, they’ll need to develop experience and new standards tailored to their unique needs. Shekar Krishnaswamy is a senior manager at Applied Materials responsible for business development and pre-sales of factory automation products and solutions. He has over 27 years of experience in all aspects of semiconductor manufacturing including wafer fab manufacturing, bump, assembly and test. His specific areas of expertise are traditional industrial engineering methods as well as systems-related methodologies such as modeling, scheduling, dispatching and factory automation. Prior to Applied Materials, Shekar held senior technical and management positions at IBM, Motorola and AMD, including management of corporate operations research departments supporting factory and service groups. Shekar has a bachelor’s degree in mechanical engineering and a master’s degree in industrial engineering and operations research. Note: SEMI has a Smart Manufacturing Technology Community. For more information or to get involved, click here.

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.

Korea is on track to top all other regions in fab investment, spending $63 billion between 2017 and 2020, with powerhouses Samsung Electronics Co. and SK Hynix leading the way, according to latest World Fab Forecast Report by SEMI. Samsung Electronics increased fab investments $770 million to $12 billion this year, and SK Hynix upped its spending a significant $2.8 billion to $7.25 billion in 2018.Korea's investment companies anticipate continued growth for both companies in the second half of 2018.Under this halo of extraordinary investment, nearly 380 SEMI Korea members and industry analysts gathered for 2018 SEMI Korea Members Day on September 13 to share insights on semiconductor market trends and new technologies that could help members bolster their competitiveness. Following are key takeaways from the event. Korea semiconductor market to grow 16% in 2018That’s according to IDC Korea VP Kim Soo-kyung, who noted that data center, memory and Internet of Things (IoT) are becoming key growth drivers for the semiconductor industry. He encouraged semiconductor companies to closely track development of automotive technology and the industry semiconductor market, both key growth areas. SEMI Korea president H.D. Cho opens SEMI Korea Members Day 2018 Continuing fab investment will lead to oversupply, but display will shineMarket entry by Chinese companies will also spur the oversupply, said Jeong Won-Seok, an analyst at HI Investment Corp. He noted that the oversupply will force Korea into stiffer competition with other regions. However, with OLED used for a wide variety of devices and the display industry seeing rapid growth, the sector will remain ripe for growth among Korean companies.Interconnecting various applications is a big semiconductor industry trendThe need for these interconnections will stand out in the mobility and high-performance computing (HPC) markets, said Park Sung-Soon, principal research fellow at Amkor Technology Korea, who addressed trends in packaging technology. He also emphasized interconnection cost efficiency as key to maximizing competitiveness.Smart Manufacturing is driving mass customizationAs semiconductor industry growth continues, production methods are shifting from ‘mass production’ to ‘mass customization,’ increasing the importance of Smart Manufacturing in driving greater production efficiency, noted BISTel VP Jeon Kyeong-Sik. Building a Smart Manufacturing platform to support large-scale production of specialized database and artificial intelligence (AI) chips will boost production efficiency, reduce costs and improve risk management. Virtual simulation will be a key enabling technology. SEMI analyst Clark Tseng presenting at SEMI Korea Members Day 2018 Surge in data volume and technology advances to drive long-term semiconductor industry growthThese key industry drivers will continue to power fab investment growth, with spending focused on 3D NAND, DRAM, and foundry, said Clark Tseng, director of Industry Research and Statistics at SEMI. China alone will see eye-watering growth with the region’s investments in domestic companies surging 46% from 2018 to 2019 and fab investment by Chinese domestic companies outpacing spending by foreign companies in China, Tseng predicted. SEMI membership rises with industry growthCulminating the event, SEMI Korea president H.D. Cho said, "With the growth of the semiconductor market, the number of SEMI members is gradually increasing, and we will help member companies grow with various activities such as Korea Members Day.”Jaegwan Shim is a marketing specialist at SEMI Korea.

Advances in areas from materials, packaging and testing to devices, equipment and standards must come together to enable smart factories and new capabilities in the microelectronics manufacturing supply chain. The secure transfer of information between factories is an important part of creating the “digital thread” that will give rise to higher yield, faster cycle times and reduced cost. At SEMICON West 2018, Tom Salmon, Vice President of Collaborative Technology Initiatives at SEMI, discusses his group’s work across the supply chain – including semiconductor front-end, packaging and test, as well as PCB and final product assembly – to accelerate the development of smart manufacturing. Ariana Raftopoulos is a marketing manager at SEMI.

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.

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.