traceability

Product lifecycle management is a well-adopted methodology used in mechanical design. Until recently, it was not widely used in the semiconductor industry. That all changed when Methodics created intellectual property lifecycle management for tracking and analysis of semiconductor IP and design.

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.

Ride the Wave of Smarter Manufacturing The year 2020 sparked a tremendous acceleration in the digital transformation worldwide, driving a sharp rise in demand for semiconductors and escalating pressure on chip factories to reduce manual functions on the shop floor. The mindset of the semiconductor industry saw a remarkable shift as it recognized with heightened urgency the need to deploy data-driven visualization, analysis, scheduling and dispatching solutions to increase automation to improve production speed and efficiency. Amidst the new excitement around Industry 4.0, chip manufacturers are rapidly deploying new technologies including IIoT, big data, machine learning and Autonomous Intelligent Vehicles (AIVs). Yet for many chip manufacturers, the path to building a smart factory is far from clear because they lack an overall digital transformation strategy. Smart manufacturing is a broad concept covering an array of technologies and solutions, making a holistic, mid- to long-term digitalization strategy rooted in the overall business strategy crucial. There are no shortcuts that can move a manufacturer instantly to Industry 4.0. Instead, this transformation is a step-by-step undertaking with a natural evolution. Some Factory Tasks Must Remain Manual – For Now The semiconductor industry has reached a point where manual processes are no longer efficient enough to support mass chip customization and remote operations. The many technological and standardization advances behind automation can help streamline some of a factory’s most labor-intensive tasks including the loading or unloading of machines or lot tracking and data collection while reducing operational costs. Still, some tasks remain very difficult to automate. For example, handling errors and exceptions presents the greatest challenge since some errors are hard to anticipate. What’s more, the cost of automating error handling can be prohibitive. Eliminating Gaps in Connectivity Often, critical data sources aren’t available due to lack of equipment integration, incomplete product quality monitoring or gaps in material tracking. Closing these gaps in connectivity enables the collection of data and provides rich, reliable information for analysis and reporting that can drive continuous operational improvements, optimizations and efficiencies throughout a factory. But keep in mind that data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even when data is available, it might be still difficult to make decisions or implement improvements if it is in siloed systems that require manual processes to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. Manual integration is not only ineffective but costly, draining time, human resources and money from the factory. The right contextual information for the data is vital to unleash its potential and make improvements possible. Dispersed solutions cannot control processes because they span functional areas and people, physical and business entities. Backbone software for shop-floor operations that controls all other applications is central to smart manufacturing. Data-Driven Manufacturing The semiconductor industry is expert in data collection and leads many other industries in this area. The problem is often that chip companies use only a fraction of the information they collect for the analysis and insights needed to improve operational efficiency. By comprehensively integrating all distributed data into a single version of truth – in one location where it is always available – companies can make data analysis and problem solving almost frictionless. Keep in mind that data platforms and edge solutions, within the context of manufacturing, will not be adopted as part of a greenfield initiative. Building a solid automation architecture is only feasible and beneficial by deploying new technologies such as machine learning and artificial intelligence (AI). Analysis of historical data provides important context and reveals deviations such as unexpected process time, uncommon material accumulations or issues with material transport. By integrating swift control actions for new data point collected, manufacturing operations can shift from reactive problem-solving to proactive analysis and operational improvements. The tremendous increase in interest and investment in AI for manufacturing automation only became possible with the availability of low-cost sensors that generate huge volumes of data and solutions for storing and processing that at low cost. AI and other leading-edge technologies transform the tedious but critical process of extracting insights from data, making it instantaneous, streamlined and achievable for every manufacturer. The maturity of smart manufacturing hinges on the extent to which a factory is data-driven. This requires foundational investments to improve traceability, connectivity and real-time operations – and finally making sure that data helps us what to do and when to do it. Ricco WALTER is managing director of SYSTEMA Automation in Singapore.

Nexperia became a standalone company about four years ago after our divestiture from NXP Semiconductors. Last year we started our journey towards smart manufacturing at our back-end factories in Asia by developing a roadmap to help steer us in the right direction.Our first step to creating a convincing and workable smart manufacturing roadmap was to define the very meaning of smart manufacturing to Nexperia. Since the definition of smart manufacturing varies widely, we started by looking at two different and distinct technology adaptations: Physical automation Data-driven manufacturing, or using analytics at the core to develop and adopt machine learning and artificial intelligence (AI) models It is important to find the right balance of investments between physical automation and data-driven manufacturing to steer clear of deployment inefficiencies since only connected solutions deliver full value. Our approach involved the following high-level steps. Meeting with internal management teams for their inputs and examining factory needs and maturity Meeting with other semiconductor factory operators, subcontractors and partners to review their smart manufacturing approaches and challenges Evaluating our needs and status against the Singapore Smart Industry Readiness Index model Physical AutomationEvaluating the maturity of available solutions and adaptions by the industry and our own shop floor helped simplify the thought process quite well. Logistic automation is not new. Very mature solutions, even for custom layouts and preferences, are readily available. Shop floor automation is far more difficult than logistic automation since variability is simply too high. Traditional shop floor investments were always driven from quality or OEE perspectives and not necessarily very well connected. Our approach is outside-in – deploy logistic automation first and then move to the shop floor.Data-Driven ManufacturingHow smart manufacturing becomes depends on the extent to which a factory is data-driven. Enabling data-driven manufacturing requires foundational investments to improve traceability, connectivity and real-time operations. We believe real-time awareness can drive machine-level and closed-loop process control critical for predictive, cognitive control of the shop floor.Real-Time Awareness and Traceability is at the CoreDeveloping real-time awareness requires wide-ranging manufacturing protocols. The following focus areas have helped us simplify the challenge: Connectivity Core systems for areas including MES, quality and SAP Analytics and AI Digital shop floor featuring one operator interface with real-time control systems Readiness of engineers, technicians and managers Each of these pillars has different level of complexity due to legacy equipment and systems, legacy processes and inexperience of engineers with automation. This makes deployment of data-driven operations a complex challenge. We looked at different project approaches for each of the focus areas: Core Systems – Build additional technology enablers and roll them out with prioritization planning. Analytics – Focus mainly on OEE and yield with automated root cause analysis and predictive approaches. Real-Time Control – Merge the initiative with factory-level programs to improve productivity and quality. With a strong smart manufacturing roadmap, the next challenge is to secure long-term buy-in on the plan and required investments from executive management. Visiting and otherwise connecting with peer sites that have already deployed smart manufacturing infrastructure is vital in this effort. Thanks to SEMI members, we were allowed to visit their factories with our management team for go-and-see tours since seeing is believing in the smart manufacturing journey. Our executives also met with subcontractors and vendors to better understand the value of this transformational undertaking.A long-term outlook is necessary to successfully develop a smart manufacturing roadmap, and executive commitment goes a long way to ensuring its success. We are excited about our smart manufacturing journey and believe it is a game changer for our factories.Adarsha MARPALLI is director of Factory Automation at Nexperia B.V.

This article is the second in a series highlighting the vital importance of SEMI Standards to commemorate the publication of the 1000th SEMI Standard in July 2019. Find the entire series here.Chip traceability. It’s one of the next big things for the technology industry. The benefits are enormous, and the upsides — which include enhancing yields by identifying the sources of reliability issues, fighting counterfeiting, and growing the overall technology market by enabling new applications — are plentiful.But the implementation challenges of chip traceability are also big and will require considerable effort to overcome. Perhaps the biggest hurdle of all is that we need to transcend industry fears by demonstrating that we can secure IP when it is shared across the hardware supply chain. What will drive the technology industry to make the necessary investments in traceability? “Automotive will drive traceability,” asserted Doug Suerich, product evangelist at PEER Group and an active participant in the SEMI Standards Traceability Committee. “If I had to guess, the autonomous car in particular will drive a traceability-standard effort.”Where Reliability is CriticalWhen your laptop crashes, it’s annoying. But when a car crashes because of a system failure, the damages can be severe and catastrophic It’s also one that is poised to get exponentially larger as we see ever greater amounts of silicon content in vehicles.Fortunately, everyone can agree on the nature of the solution. The industry needs to create a standard for traceability throughout the supply chain. When lives are at risk, we must find and fix the manufacturing source of any defects that affect reliability. That’s understood. Now it’s the not-so-small matter of figuring out the details.Of course, it’s not just about cars. Manufacturers and users of medical devices and military platforms also put a premium on extended, high levels of reliability. In the technology industry, however, the automotive market represents such enormous growth potential that we view it as integral to future industry success.At a market size of more than $1 trillion, automotive is steadily becoming a high-tech market as cars transform into advanced technology platforms that offer partially or fully autonomous features. Vehicles are fast becoming semiconductors on wheels. With leaders from Google to General Motors investing heavily in chip advances, the automotive industry will demand a supply chain that requires chip and device traceability from all its participants.The SEMI Technology Communities and Standards Committee have made some inroads toward solving the traceability challenge with their development and publication of a SEMI Standard enabling traceable device-level identification (ID) throughout the IC manufacturing, test, and assembly processes to the point of use in the final system. The standard is a meaningful first step but overcoming the challenges of counterfeiting and information sharing remain and will require greater industry collaboration.“It comes down to a safety issue,” said Suerich. “We need the ability to collect data across the supply chain, so we can trace down the source of a reliability issue, analyze the data and take corrective actions around applications for which safety is critical. Automotive, medical and aerospace devices need to keep working over five, 10 or even more years. For the semiconductor industry, that means redefining yield.”Traceability Roadmap“It’s going to be a major challenge to share data throughout the supply chain, not just technologically, but culturally as well,” added Suerich. “It will take a concerted effort, and we’re just in the early stages of figuring out some of the IP protection issues.”While traceability is new ground for the culture of the semiconductor industry, the automotive industry has long embraced tracing the sources of defects. In some cases, automotive suppliers have issued wide-ranging product recalls due to safety concerns. The Takata airbag defect, for example, resulted in tens of millions of recalled airbags. As the automotive and semiconductor supply chains increasingly overlap, SEMI committees and task forces are in an ideal position to model traceability best practices in after those implemented by the automotive industry.“We’re going to need an organization like SEMI to coordinate and organize this,” observed Suerich. “While we’re still in the early phases of figuring this out, the market potential around automotive has attracted a critical mass of powerful companies who want a solution. We need to standardize a way to tag which information can flow up and down the chain, and which is protected. I think we’re looking at more than five years of hard work around new standards.”Semiconductor companies are understandably cautious about sharing data related to their proprietary processes because the value of the intellectual property and need to protect data is simply higher than in many other industries. “Automotive offers the perfect confluence of factors to drive traceability in semiconductors,” Suerich concluded. “There is increasing silicon content as well as lives and big money at stake, and motivated players at leading companies and within government institutions want to see progress.”Use your voice to affect standardization in and around the microelectronics industry. Learn about SEMI International Standards – and become part of the solution. Learn more about SEMI's traceability activities. Heidi Hoffman is senior director of technology communities marketing at SEMI. Hoffman and her team shine a spotlight on the work of the more than 20 technology communities under the SEMI electronics manufacturing supply chain collaboration platform. Actively engaging community members in marketing programs that showcase their unique value, Hoffman’s team helps companies grow and prosper through the power of connection, collaboration and innovation.