OSAT

Spend any time with Ansys’ John Lee, Rich Goldman or Marc Swinnen and you’ll hear plenty of optimism about the semiconductor industry even though they tick off a long list of looming design challenges. The need for reliable and effective electronic systems, they emphasize, is great and runs through high tech, aerospace and defense, automotive, IoT and 5G with communications being a common denominator. The three are especially bullish these days on changing market dynamics brought on by systems companies building company-specific bespoke, or custom, silicon. These systems companies are building chips with a different perspective and a fresh look at silicon design, a move away from the more traditional segment-specific silicon due to much more complexity. Ansys, a member of the ESD Alliance, a SEMI Technology Community, is a 4,100-employee company with a comprehensive portfolio of multiphysics engineering simulation software for product design, testing and operation products and services. John, Rich, Marc and I focused on Ansys’ semiconductor and electronics segment for our conversation. Smith: When did you notice the move by systems companies to build their own chips? What drives this trend? Lee: The inflection point was about three years ago when hyperscale data center and system companies recognized they needed an enterprise system design platform. They are designing bespoke silicon, driven to do this for cost efficiencies and to avoid relying on outside suppliers. They also want differentiation based on their specific platform needs so they can optimize compute power to their specific needs. Smith: What is driving the trend for multiphysics experience to ensure effective and reliable electronic systems? Lee: The increasing need for multiphysics analysis is acute. The physics of 3D IC, for example, brings in mechanical engineering with the convergence of mechanical and electrical as 3D emerges at the intersection of IC and System. As a result, physics becomes a necessity to analyze the stability of the chip in the package. Goldman: As well, the move to stacked chips, 3D IC and wafer-on-wafer requires thermal, electromagnetic and mechanical analysis in addition to the traditional analysis for function, performance and power. They all need to be analyzed together, not serially. It becomes multiphysics, not multiple physics. Smith: Two distinctly different disciplines – multiple physics and multiphysics – are needed for semiconductor design. How are they different? Why the need now? Swinnen: Multiple physics refers to the sheer breadth of physics that is now needed to analyze from the IC up to the largest system whereas multiphysics refers to the capability to analyze several physical effects concurrently, accounting for their impact on the design and interactions between various physics. Multiphysics are necessary to analyze the full context of the system environment – from nanometers to kilometers – for multi-chip packaging, chip-to-package-to-silicon and systems with multi-domain guidance. Goldman: A self-driving car, as an illustration, includes AI systems-on-chip, solid-state sensors, infotainment systems and radar/lidar detectors that must all work in the rain, the heat and the bitter cold. Smith: Why are design groups being reorganized to include expertise in mechanical and electromagnetic issues? Swinnen: Complexity has exploded, driven by a long list of technical requirements and, perhaps, mischaracterization. Goldman: Just consider the system on chip, mischaracterized by the semiconductor industry. The chip is never a system by itself. Rather, it is a complex component in a larger system and must be analyzed in that context. 3D IC is where this comes together and forces a recognition of physics outside the traditional scope of SoC design. 3D IC chips are much closer together on the board and it takes multiphysics embedded into the workflow of semiconductor design, packaging, system design and 3D IC to ensure they work reliably and efficiently. Smith: What is the solution? Goldman: It’s clear a specialized digital thread is necessary to move disparate groups with expertise in systems, physics and silicon together. Today, these groups or disciplines might not exist in the same company, whether it be a foundry, fabless or outsourced semiconductor assembly and test (OSAT) company. Lee: In order to unify the entire system design environment, a cloud-based, open and extensible heterogenous enterprise compute platform is required. It is similar to the SaaS-based business model and known as Simulation-as-a-Service (also SaaS). While vertical integration of design groups is already taking place at leading system design houses, there have also been advances in electronic design tools. These are starting to offer more comprehensive multiphysics capabilities including thermal, fluid dynamics (CFD), mechanical stress and reliability analysis in a single analysis cockpit. Today’s system designers face two platform challenges: First, they need an environment that is open enough to accept analysis results from multiple sources so that they can be overlapped and cross-analyzed. Second, the design platform must have the capacity to handle the enormous amounts of data generated by the latest 3-nanometer chips and 3D IC systems, and this implies an intimate coupling to elastic cloud computing. The days of an engineer writing Perl scripts and handing it off to someone else are gone. We believe that the industry is responding to this challenge with a new generation of design platforms that a cloud-native, open and extensible to allow heterogenous enterprise design. We are definitely at an inflection point in electronic design today, but the electronic industry has faced these before an we are confident it will master these challenges as well. About Rich Goldman Rich Goldman is director of marketing for the Electronics and Semiconductor Business Unit of Ansys. He holds a Bachelor of Science degree from Syracuse University and an MBA and Master of Science degree in Engineering Management. Moscow Institute of Electronic Technology (MIET)’s first honorary professor, he is also the recipient of honorary PhD degrees from Russian-Armenian (Slavnoic) University and State Engineering University of Armenia for contributions to the advancement of Armenia’s high-tech education and economic ecosystem. Rich served on EDAC’s board of directors. About John Lee John Lee is general manager and vice president of the Ansys Electronics and Semiconductor Business Unit. Lee co-founded and served as CEO of Gear Design Solutions (now Ansys), developer of the first purpose-built big data platform for integrated circuit design. He cofounded two other startups (Mojave Design and Performance Signal Integrity), which successfully exited into companies now part of Synopsys. He holds undergraduate and graduate degrees from Carnegie Mellon University. About Marc Swinnen Marc Swinnen is director of product marketing for the Electronics and Semiconductor Division of Ansys. He holds Master degrees in Electronic Engineering and Industrial Management from KU Leuven, Belgium, as well as an MBA from San Jose State University. About Bob Smith Robert (Bob) Smith is executive director of the ESD Alliance, a SEMI Technology Community. He is responsible for the management and operations of the ESD Alliance, an international association of companies providing goods and services throughout the semiconductor design ecosystem.

The SEMI Smart Manufacturing Americas Chapter, a key driver of the Global Smart Manufacturing Initiative, accelerates awareness of digital and data-driven strategies and implementations to help speed adoption of smart manufacturing. In 2021, the Chapter will focus on expanding its work across the industry to include academic and research initiatives. The semiconductor industry saw an unprecedented focus on improving digital monitoring of manufacturing activity in 2020, partially due to COVID-19. The Americas Chapter shared case studies on new tools and techniques for social distancing in fabs, aides for remote maintenance, and tips for remote workers. The Chapter also introduced its three pillars of Sensing, Connecting and Predicting and offered related programs. The Global Smart Manufacturing Conference (GSMC) highlighted the significance of universities and research institutions in the development of smart manufacturing with their focus on joint research for broad dissemination. To help drive smart manufacturing advances, at GSMC several offered non-proprietary tutorials on topic including the following: Integrating sensors for acquisition – CEA-Leti Applying new AI and ML tools and strategies to manufacturing – Binghamton University Digital tools for planning, qualifying and management and scheduling in fabs – MINES Saint-Étienne. Adding AI tools to robot work in a smart factory – KAIST Institutes By continuously highlighting the activities of these and other institutions through presentations, interviews, articles and blog posts, we will draw more attention to what is on the horizon for smart manufacturing in 2021. The SEMI Smart Manufacturing Americas Chapter also plans to elevate activities important to the Outsourced Semiconductor Assembly and Test (OSAT), Surface-Mount Technology (SMT) and Printed Circuit Board Assembly (PCBA) segments of the industry including programs on inspection, traceability and the SEMI SMT-ELS Standard for SMT automation. Thurston Taylor, marketing expert at Tokyo Electron and Vice Chair of the Americas Chapter, notes that “With increasingly more demanding requirements for bump, assembly and test, smart manufacturing and applied data science are necessary to achieve back-end goals now and in the future.” Also, many companies are implementing smart manufacturing applications and assessing various strategies to increase their smart manufacturing capabilities. Members of the Americas Chapter plan to review and develop self-assessment documents and maturity models that apply to front-end wafer fabs all the way through packaging and assembly facilities. “Moving forward it is imperative for all of us to up the intensity on specific ROI vectors such as quality, cost, productivity, sustainability and safety leveraging our smart manufacturing SEMI framework of Sensing, Connecting and Predicting,” said noted Bobby Mitra, worldwide director of Smart Manufacturing at Texas Instruments and Americas Chapter Chair. “By offering special flagship events, invited talks, ROI case-studies and ROI criteria in maturity models, we’ll bring high value to the smart manufacturing industry.” Chapter members also will begin mapping the skills needed to implement and support increasingly digital manufacturing capabilities, including any new skill sets, to help companies develop their hiring, training and management strategies. The mapping effort aims to support companies in building a strong pipeline of employees who can efficiently manage and operate smart manufacturing facilities. For its part, the Americas Chapter’s Go Green Subcommittee will focus on applying smart manufacturing technology to reducing the electronic industry’s carbon footprint by accurately tracking energy waste improving overall fab efficiency. Stay tuned for details on activities planned for our chapters in Europe, China, Japan, Korea, Southeast Asia and Taiwan. To learn more about each chapter and how to get involved, please visit the SEMI Smart Manufacturing Hub and sign up for our newsletter. Ayo Kajopaiye is senior project coordinator, Collaborative Technology Platforms, at SEMI.

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.

Outsourced Semiconductor Assembly and Test (OSAT) service providers experienced strong growth in 2017, but will this growth continue? In the last few years, OSAT growth has been driven by shipments for packages found in smartphones, but this market is slowing. What will replace it? Growth in power devices is strong and electronic content in vehicles is increasing. Will OSATs participate in this growth? Many OSATs have plants dedicated to automotive package assembly and will see continued growth. Growing demand for connectivity everywhere, called IoT, is generating large amounts of data, creating the need for more servers and datacenters. The adoption of Artificial Intelligence (AI) across a broad range of applications is driving demand for high-performance packages, but will this assembly take place at the OSATs or foundries? In the third and fourth quarters of 2017, growth in cryptocurrency provided unanticipated revenue for a number of OSATs. Given that the most well-known crypto mining companies and the biggest mining pools are all based in China, several OSATs, including major Taiwanese and Chinese service providers, experienced revenue growth in 2017 directly attributed to the assembly of ASICs in flip chip scale packages (FC-CSPs) and GPUs in flip chip ball grid arrays (FC-BGAs) for the cryptocurrency market. However, the first and second quarter of this year has seen decreased demand for GPUs and ASICs for this application. The assembly of packages for cryptocurrency slowed considerably in the first half of the year and therefore can’t be counted on to add as much to the revenue base as in the previous year. Going into the latter half of the year, the demand for Crypto ASICs is expected to pick up as new generation of 7nm chips will drive new investment and replacement cycle while crypto-mining GPU will see a further decline. Three of the top 10 OSATs, Jiangsu Changjiang Electronics Technology (JCET), Tianshui Huatian Technology (Huatian), and Tongfu Microelectronics (TFME), are based in China. China’s share of the top 10 OSATs’ revenue increased from slightly less than 23 percent in 2016 to more than 25 percent in 2017, and this trend is expected to continue. Crypto-related packaging and test business has certainly contributed a big portion of the share gain. Major OSATs such as TFME and Tianshui Huatian plan expansion in their plants and they expect to fill this added capacity in a broad range of packages. Huatian’s new Nanjing plant will include assembly for memory packages. TFME plans to set up a plant in Xiamen, Fujian Province to provide bumping, wafer level packaging, and system-in-packaging (SiP) services. Tracking the capabilities of OSATs is increasingly important. SEMI and TechSearch International have introduced a new Worldwide OSAT Manufacturing Site Database that provides listings of OSAT facility locations and package and test options in each factory. This database indicates the specific packages offered at each location. Finding plants that offer automotive qualified assembly is also possible with the database. Companies that offer bumping and wafer level packaging are identified. Over 120 companies and 300 facilities are tracked in this database covering both OSAT packaging and test facilities. For additional information about this informative database, please visit https://discover.semi.org/osat-database-registration.html E. Jan Vardaman is president of TechSearch International, Inc., and Clark Tseng is director of Industry Research and Statistics at SEMI.

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 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