Coventor

Semiconductor process development is no easy task, with each generation of devices more difficult and expensive to create. Traditional cycles of build-and-test development are becoming obsolete, since they are too expensive and time-consuming for the most advanced processes.The High Cost of Process DevelopmentMost chip designers developing new products rely on existing manufacturing processes, but someone had to create those processes to make the designs possible. The goal of process development is to create new semiconductor manufacturing processes that provide high yield while achieving the required device performance. In contrast to new chip design, however, it requires an entirely different set of engineers and skills.The traditional approach to process development involves building multiple test wafers to determine the ideal process for a given device. After one set of wafers is fabricated and analyzed, insights from the previous round help to refine process steps for another round of fabrication. Due to smaller feature sizes, each new process generation is more sensitive to variation. This adds even more complexity because smaller feature sizes and parasitic effects require more measurements and testing as well as additional fabrication. The cycle is repeated many times before the entire process flow can be finalized, making it time- and cost-intensive, especially for the most advanced technology nodes.Testing Virtual Wafers Instead of Real WafersToday, there is an alternative to this slow, expensive way of doing things. Virtual fabrication lets computers simulate all of the processing that occurs when real wafers are built. These virtual models allow semiconductor process engineers to test manufacturing equipment settings with far greater variation than is possible in a physical fab. Designers can simulate the entire process flow, running the equivalent of thousands of wafers in days instead of months. Designers can quickly see graphical animations to visualize process steps, modify process recipes and device geometries, and measure how these changes affect electrical behavior.Improving Yield Using Statistics in Virtual Wafer FabricationBecause of the high volume of data generated, designers are turning to statistical analysis to provide greater confidence in their choice of process settings. Defects and random variations can be modeled in a virtual fab in a way that’s not possible in a real fab, letting developers test the sensitivity of the device structures against the unpredictable aspects of processing.There’s more than one approach to optimizing the process settings used in a new memory or logic fabrication sequence. The simplest one involves taking a single variable and exploring its effects. Critical dimensions (CDs), for example, establish those feature sizes of a device that ensure desired electrical performance. A particular dimension can be swept from low to high values – developers can then measure the effects of that range on device behaviors such as threshold voltage. This allows developers to ensure that the electrical behavior of their device design addresses the range of expected feature sizes and variability. The interactions with intersecting process steps can also be tested for further validation, since these interactions can lead to unanticipated device performance.But, in reality, this approach isn’t sufficient for studying the complex web of interactions between process steps and the resulting structures.A second approach leverages Monte Carlo analysis, randomly varying a wide range of process and device parameters and calculating the resulting device geometry and performance. This data can be used to automatically identify the process and design settings needed to achieve yield and performance goals. It’s an area where simulation shines, providing a useful way to test the interactions between many different processes.Statistical experiments using virtual fabrication illustrate step-by-step methodology to optimize process and design settingsVirtual Fabrication PlatformSEMulator3D is a virtual fabrication platform created by Coventor, a Lam Research company. It allows the definition of all process steps, the modeling of devices, the collection of metrics, electrical and device analysis, the statistical analysis of results, and the visualization of process steps through graphical animation. Today, semiconductor companies use it for both optimizing and scaling leading process nodes and for developing advanced new technologies like GAA (Gate-All-Around) transistors.The ability to do this work virtually is the future of semiconductor process development. Virtual fabrication accelerates new process time-to-market by months, opening up market opportunities worth hundreds of millions of dollars for semiconductor companies.Visualization of process steps of a Gate-All-Around transistor shows 3D construction in SEMulator3D. To learn more about virtual fabrication and how it’s changing the future of semiconductor technology development, download our whitepaper Speeding Up Process Optimization with Virtual Fabrication.Lam Research is a longtime member of MEMS Sensors Industry Group®, (MSIG), a SEMI technology community that connects the MEMS and sensors supply network in established and emerging markets, enabling members to grow and prosper. Visit us today.David M. Fried, Ph.D., is vice president of Computational Products at Lam Research, where he is responsible for the company’s strategic direction and implementation of virtual process solutions, including the Coventor SEMulator3D virtual fabrication 3D process modeling solution. Fried leads the execution of technology strategy for technology platforms, partnerships, and external relationships. His expertise touches upon such areas as Silicon-on-Insulator (SOI), FinFETs, memory scaling, strained silicon, and process variability.Fried is a well-respected technologist in the semiconductor industry, with 60 patents to his credit and a notable 14-year career with IBM, where he was involved in successive process generations from 65-nanometer and lower. His most recent position was 22nm chief technologist for IBM’s Systems and Technology Group. He holds bachelor’s, master’s and doctoral degrees in Electrical Engineering from Cornell University.Republished with permission from Lam Research.

New MEMS-based products are constantly emerging, fueled by the Internet of Things (IoT), autonomous driving, smart manufacturing and healthcare applications. The MEMS pressure sensor market is no exception to this trend1. Its growth has been driven mainly by automotive applications such as tire pressure management system (TPMS) regulations in China, fuel and ignition systems, thermal systems, oil-pressure monitoring, and indoor and outdoor navigation systems. Easy to customize and integrate, miniature, sensitive, accurate and low-power MEMS devices are especially well-suited to the accuracy, power consumption, sensitivity and miniaturization that pressure sensors require.Yet MEMS design also presents some specialized challenges, such as a strong coupling between fabrication technology and design. Complex physical structures that exhibit non-linear behavior, custom packaging requirements, and a final product that requires integration with surrounding CMOS circuitry are just a few examples. What’s more, there is a lack of standardized processes and process validation in MEMS design ecosystems. Pressure Sensor (Courtesy: X-FAB) As with other products based on MEMS technology, designers must increasingly customize pressure sensors for higher performance – sensitivity and linearity, in this case – while decreasing their package size. Designers can accomplish the task by studying sensor performance and manufacturability using computer models prior to fabrication. This can ensure that the sensor meets its required specifications while simultaneously reducing manufacturing cycles and cost.The Power of CollaborationThis is where strong collaboration among EDA providers, MEMS technologists and designers delivers tangible benefits. EDA providers and MEMS foundries can collectively help MEMS designers to incorporate foundry process constraints into their designs.In the semiconductor industry, first-pass successful silicon relies on standardized manufacturing processes, thorough technology characterization, accurate model generation, established simulation and verification, and extensive reuse of proven design blocks. In the MEMS world, where processes and products are developed concurrently, and processes change with every product, is it possible to adopt standardized processes, design methodologies, and tools that enable efficient reuse of existing technology and design knowledge? The challenge lies in maintaining the flexibility to optimize products for a diverse array of requirements. The ideal design platform should ease sharing of technology and design data between the foundry and its customers, enabling two-way collaborative development and allowing foundry technologists to easily perform a feasibility assessment of a customer’s project. This approach offers important benefits, allowing designers to explore and evaluate the suitability of a foundry’s process technology in their unique application. It also supports accurate prediction of device performance prior to fabrication and reduces costly build-and-test cycles. Combining standardized manufacturing processes, MEMS process design kits (PDKs), and a proven design flow are the starting point for development of manufacturing-ready designs.A Real-Life Example using Pressure SensorsAn EDA company, Coventor (a Lam Research company), along with MEMS foundry partner X-FAB, collaborated to develop a PDK that would ensure that manufacturing constraints are automatically considered early in their design process. The design flow is based upon an X-FAB fabrication platform that supports multiple process options for the manufacturing of absolute and relative MEMS pressure sensors. The PDK is a “golden container” for all the process and material characteristics of the silicon membrane and substrate, glass, passivation layers, and piezoresistive components. It enforces material properties and guarantees their correct implementation during the simulation. It also includes a component library containing ready-to-use, 3D parameterized devices (such as membranes and resistors), all pre-designed with foundry-supported materials to support their respective design rules. The components are readily partitioned for optimized meshing and simulation, saving design and simulation time. Figure 1: The elements and design flow of the PDK designed by Coventor and X-FAB. (Courtesy: Coventor)Designers can use components from the library to create a custom design — which might include different membrane shapes and sizes, and resistors of varying shape, size and position — to simulate the impact of different technology variants (such as resistor doping profiles, membrane and substrate thickness, glass material properties, and passivation schemes). This allows them to anticipate the effect of these design changes on sensor sensitivity for varying pressure and temperature regimes.Extensive validation of the pressure sensor design platform is currently underway. So far, the simulations have exhibited very good correlation to actual device measurements across a range of pressure and temperature conditions, including predictions of non-linear behavior for various pressure sensor designs. At the same time, the simulation accounts for mechanical membrane properties and piezoresistivity. With this type of design platform, a foundry can provide guidelines to help customers select both the fab technology and design features that lead to an optimal design solution. Figure 2: Simulation results depicting mechanical displacement in a pressure sensor design (Courtesy: X-FAB) Let’s Face the Next Challenges…A complete design platform for MEMS must eventually include not only MEMS device design, but system integration functions, such as the application-specific integrated circuit (ASIC) design and packaging/assembly of the product. In addition to the design verification that the PDK provides, additional partnerships among foundries, integrated device manufacturers (IDMs), research centers, equipment suppliers, and EDA vendors will help to define requirements and solutions that address every level of design and production. These might include tasks such as describing standardized material properties and process specifications, creating accurate foundry-proven design models, and defining requirements for system-level simulation. In the future, PDK simulations might even include up to tape-out and physical verification. To learn more about this collaborative PDK development work, please click here for the whitepaper.Christine Dufour, MEMS PDK Program Manager, CoventorChristine Dufour is the MEMS PDK program manager at Coventor. She has more than 20 years of experience in the semiconductor industry, leading process design kit development for BiCMOS and CMOS processes at several major semiconductor companies. Ms. Dufour has also worked as a product manager in the RF design environment area. In addition to her extensive experience in MEMS PDK development, she is an expert in all aspects of MEMS design flow and design tool development. Ms. Dufour received an engineering degree at Technological University of Compiegne.For more information on Coventor, a Lam Research Company, visit: https://www.coventor.com/ Viraja Sharma, Development Engineer, MEMS Simulation Design, X-FABViraja Sharma is a development engineer for MEMS Simulation Design at X-FAB. Her work involves the design and simulation of MEMS inertial and pressure sensors. Prior to her tenure at X-FAB, Ms. Sharma performed similar duties for other semiconductor companies. She received her Master of Science degree in Micro and Nano Systems from TU Chemnitz, where she studied MEMS and micro technologies.For more information on X-FAB, visit: https://www.xfab.comCoventor and X-FAB are members of SEMI-MEMS Sensors Industry Group that connects the MEMS and sensors supply network, enabling members to address common industry challenges and explore new markets. 1 Market research firm Yole Développement predicts that MEMS pressure sensors alone will become a $2 billion market by 2023. See: https://yole-i-micronews-com.osu.eu-west 2.outscale.com/uploads/2019/01/YD18018_MEMS_Pressure_Sensor_Market_Yole_Developpement_2018_Sample.pdf

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

Self-driving cars have been all the rage in both the trade and popular press in recent years. I prefer the term “autonomous vehicles,” which more broadly captures the possibilities, encompassing not only small passenger vehicles but mass transit and industrial vehicles as well. Depending on who’s talking, we will all be riding in fully autonomous vehicles in five to 25 years.The five-year estimates come from startups eager to raise venture capital while the 25-year estimates stem from Tier 1 automotive suppliers who tend to be more conservative in outlook. Regardless of the timeframe, a multitude of investors – national governments, venture capitalists and companies – are dedicating significant capital and effort to make autonomous vehicles a reality.I must admit that I did not fully grasp the enthusiasm for self-driving cars until last year. First, I’ve always enjoyed driving, unless I’m in stop-and-go traffic, so I couldn’t imagine relinquishing the task. Second, I’ve deliberately arranged my life to spend minimal time in my car. However, traffic has become much heavier in my metropolitan area (Boston), and I know that many people in cities around the world face longer commutes and waste more time in gridlock.What is the solution to this problem that is only getting worse? I had an epiphany while walking through Shinigawa Station in Tokyo, one of the busiest train stations in the world. Dense streams of people crisscrossed the station on their individual paths, managing to avoid collisions without the aid of traffic controls. Evidently, humans have an innate collision-avoidance ability that makes traffic controls for pedestrian crowds unnecessary. If autonomous vehicles could achieve the same excellence in collision-avoidance, we could potentially reduce or eliminate traffic controls for vehicular traffic, providing a huge gain in transportation efficiency and relief from gridlock.Sensors as core building blocksNew and improved sensors, many based on micro-electromechanical systems (MEMS) technology, are key to achieving this vision. While MEMS inertial sensors (such as accelerometers and gyros) are already integral to the core safety systems in conventional vehicles, they are also essential to improved self-navigation in autonomous vehicles.The challenge for MEMS suppliers is to deliver inertial sensors that meet the requirements for self-navigation systems, which are different and more demanding than for safety systems.Pinpointing a vehicle’s position requires “dead reckoning” based on inertial sensor signals as a supplement to GPS input. Undesirable drift in the inertial sensor signals due to mechanical quadrature, temperature sensitivity and noise can quickly add up to a large error in position that may result in a collision. To meet the more rigorous requirements for autonomous vehicles, suppliers must design MEMS inertial sensors that are substantially more precise and resistant to drift. This requires design software that is both extremely accurate and fast, as well as increasingly precise and reliable manufacturing capabilities.Other MEMS-based devices, such as micromirrors and micro ultrasound transducers (MUTs), are also promising options for implementing vision and range-finding systems in autonomous vehicles. These sensing systems are needed for building electronic versions of the human collision-avoidance abilities that I witnessed in Shinigawa Station – and it is these systems that autonomous vehicles must emulate.When will self-driving cars become a reality? Aside from the provocative question that got you to read this far, I don’t have a definitive answer. It will undoubtedly occur in phases, ranging from the driver-augmentation systems available in today’s cars to the full autonomy and ubiquity that will allow reduction of traffic controls in 20 years or more. It is clear that the ultimate goals for autonomous vehicles are highly worthwhile, and that achieving those goals will require better-performing and more diverse MEMS sensors. Stephen (Steve) Breit, Ph.D. is Senior Director, MEMS Business, at Coventor, a Lam Research Company. Steve has been responsible for overseeing development and delivery of Coventor’s industry-leading software tools for MEMS design automation since joining Coventor in 2000. Steve holds numerous patents on software systems and methods for MEMS design automation and virtual fabrication. He holds a Ph.D. in Ocean Engineering from MIT and a B.S. in Naval Architecture and Marine Engineering from Webb Institute.For more information, visit: https://www.coventor.com

The fast-maturing hardware and software that are enabling practical applications of equipment intelligence and machine learning mean disruptive change for microelectronics manufacturing. But first comes the basic work of building the basic infrastructure, figuring out IP separation, and learning to solve physical problems in the digital world. Just how much can the semiconductor industry leverage industrial IoT practices from other industries? Common wisdom may be that industrial software solutions aren’t well suited to the IC sector’s complex needs. But GE Digital enterprise account executive Luke Smaul, currently working with Intel, argues that semiconductor fabs and toolmakers are dealing with similar issues as GE did when it first started working with Delta Airlines to monitor the GE engines on Delta planes. Smaul will speak at SEMICON West about GE’s work with Intel over the past few years and, in particular, how its solution for cloud security and IP separation can work for ICs. “GE learned to provide IP security and separation in the aviation space with its suppliers, which moved us all up the value chain, providing a big engine for growth,” says Smaul, who started his career as an IC engineer. “GE Aviation saw a 25 percent increase in issue detection rates by leveraging the same common platform. We’ve shown that we can protect Intel intellectual property in its own cloud space and control who can access what.” A toolmaker can access only particular fab data as needed for analysis, and then can reveal only the output from the analysis and a subset of supporting data. “IP separation has to happen, and it will unlock huge added value,” Smaul says. GE’s Predix solution aims to supply an easy-to-use, plug-and-play system for analytics to enable a yield engineer without a deep data background to select a supported sensor, a gateway to connect automatically to the cloud, and an analytics application to test a hypothesis of how the collected data relates to yield. “This empowers the yield engineer to use and unlock information for a quick improvement, even for simple things such as looking at the impact of degradation of fan performance over time on yield,” says Smaul. “Though the scope may be small, the impact on yield in aggregate, and when scaled, is large.” “There needs to be much more collaboration across the industry to make this work, and to share best practices,” says Smaul. “Just as GE moved from selling gas turbines to selling power-as-a-service, vendors of other big, expensive assets like IC equipment will likely change their business model from selling tools towards selling yield-as-a-service. This will simplify life for the fab while bringing the toolmaker more opportunity to sell improved capabilities on existing tools.” More human intelligence makes AI smarter Applying AI neural network approaches such as deep learning to predict outcomes from digital models is enabling disruptive advances in speech and image recognition, but applying it to complex IC manufacturing problems such as predictive maintenance has been a challenge. These neural networks require massive amounts of data to train, and the IC sector doesn’t really have big data, just a lot of little data clusters due to the dynamics and context richness of processes. This data is difficult to combine for analysis. In addition, the neural network provides only an answer but can’t explain why, notes Michael Armacost, managing director of advanced service engineering at Applied Materials. “We’ve learned that it works better if we do not ignore what we know already, but rather incorporate expert knowledge in a structured way to help us focus on the key features and the key data,” says Armacost, who will also speak in the program. This includes choosing the most important steps to include in the model, identifying the limited data to collect and how to filter the data for outliers, and then selecting the final parameters and features, adjusting the limits, and making adjustments as results drift change over time. The less data needed, the better for the complicated issue of IP protection as well. The big gains from these new analysis approaches will likely require data from more than one company and supporting security for remote connectivity. “Some end users are attempting to do the AI all themselves, but in the long term there will need to be collaboration across companies,” says James Moyne, University of Michigan professor and consultant to Applied Materials, another speaker. Collaboration will need to balance the value of the solution against the risk of compromising IP. “The low-hanging fruit are applications such as predictive maintenance in areas that do not involve high-priority IP. Another approach will be to limit the amount of shared data needed – to first build the model on a wide range of data, but then to use only a very small amount of data to operate the models.” Ready-made models could speed the process Coventor’s semiconductor process models are finding initial applications in R D whereby companies use the simulation to understand the effect of process variation on their complex designs. Instead of running dozens of actual wafers to optimize semiconductor processes, users can instead quickly simulate the results of complex process interactions on their design. Going forward, the process models could find a wide range of applications, from accelerating stabilization of new processes in the fab to enabling real-time co-optimized control across previously independent unit steps to improve wafer uniformity. “This improved uniformity across wafers and equipment could potentially reduce the need for costly physical silicon validation,” suggests Joseph Ervin, Coventor director, semiconductor process and integration, another SEMICON speaker. “Making use of in-situ metrology for real-time control also demands a digital model to process and analyze the collected data for quick response. This area has tremendous potential for improving semiconductor process control.” SEMICON West features a Smart Manufacturing Pavilion with displays and three full days of speakers on building the infrastructure needed to enable disruptive artificial intelligence in the microelectronics sector. www.semiconwest.org The SEMICON West Smart Manufacturing Pavilion features interactive Touch Liquid Crystal Displays (TLCD) and working production equipment on the floor from Bosch Rexroth, Cimetrix, Rudolph Technologies, Inficon/Final Phase Systems, OMRON, DISCO and Edwards Vacuum. For information on the SEMI Smart Manufacturing Initiative and how to get involved, please click here. Paula Doe, SEMI