Industry 4.0

Introduction Automated production in electronics manufacturing can produce high-quality products, but it might lead to a particular failure without human interventions. With the rapid technology development, such as the Industrial Internet of Things (IIoT), big data analysis, cloud computing, artificial intelligence (AI), many manufacturing processes can be more intelligent, and Industry 4.0 can then be realized in the near future[1]. Smart manufacturing adopts real-time decision-making based on operational and inspectional data and integrates the entire manufacturing process as a unified framework. Then, the future manufacturing process transforms cyber-physical systems digitally and responds to any uncertain situations proactively while ensuring higher efficiency. In Surface Mount Assembly (SMA) lines, equipment status and quality data can be collected via IIoT technology. Data-driven solutions, such as AI and machine learning algorithms, can be applied to diagnose abnormal defects and adjust optimal machine parameters in response to unexpected changes/situations during production. Collaborating with various SMT industry partners, the research team at the State University of New York at Binghamton (aka Binghamton University) developed a novel framework based on AI-based closed-loop feedback control and parameter optimization to implement a smart manufacturing solution in the PCB assembly for yield and throughput improvement. This AI-based framework could provide a potential road map for data-driven process control in SMA. Machine Intelligence in SMA Each SMA process has a critical effect on the final PCB product quality and throughput. Notably, the solder printing process is a critical operation because over 60% of the PCB assembly soldering defects can be traced back to this stage. An inadequate volume of solder paste transferred to any PCB pad is a printing fault, which leads to board failure and substantial reworking and repair costs. The pick and place (P P) process is the highest cost procedure, including expensive machine investment and extended production time. In the soldering reflow process (SRP), the reflow oven temperature and other related settings determine the solder joints' quality and reliability. Hence, multiple inspection machines in the SMA processes have been introduced, including solder paste inspection (SPI) and automated optical inspection (AOI) machines. Particularly, two independent AOIs could be employed to detect the components' defects before and after SRP. Because many electronics components become small-scale (e.g., ), more assembly-related failures are often observed in recent SMA processes. The Smart Electronics Manufacturing Laboratory (SEML) at Binghamton University is fully equipped with two solder paste printers, two chip-mounters, and a reflow oven along with SPI and AOI machines. The research team tested more than 8,000 PCB at SEML. The results show that numerical methods based only on physical properties might have practical limitations in explaining small-scale components' behavioral patterns. It might be caused by unknown environmental factors (e.g., temperature and humidity), machine calibration, measurement accuracies, vibrations, etc., which could have influenced the quality of the SMA outcomes. However, recent research shows that AI-based methods can increase product quality up to 35%, reduce scrap rates, and optimize fab operations in semiconductor manufacturing, compared to traditional approaches[2]. It implies that a data-driven intelligent SMA process control has the potential to advance SMA processes. The goal of the smart SMA is to maintain optimized settings in both offline and online scenarios. The AI and data analytics solution can optimize all SMA process parameters before production (i.e., offline control) and during production (i.e., online control). The overall schematic of the AI-based closed-loop feedback control framework is illustrated in Figure 2. Intelligent SMA Modules In the solder printing process, four machine intelligence modules are considered: Printing advising module (PAM) Printing optimization module (POM) Printing diagnosis module (PDM) Dynamic stencil cleaning process control (CPC) Figure 2. A schematic diagram of the AI-based closed-loop feedback platform Figure 3. PAM effectiveness over customer’s best-known printing parameter setting PAM aims to recommend the ideal initial setting of the printer critical parameters, such as printing speed, printing pressure, and separation speed, using hybrid machine learning and heuristics optimization techniques[3]. As a case study, the research team validated the PAM's performance with an automotive PCB testbed and compared the printing results to the best-known printing parameters. The experimental results show that PAM can achieve over 50% higher Cpk (i.e., process capability index, as shown in Figure 3). POM optimizes printing parameters in real-time by monitoring printing quality and fine-tuning offset and process parameters for adapting to dynamic conditions[4]. The experimental results show that POM achieves more than 30% production quality improvement in terms of the Cpk by adjusting printing parameters compared to the offline control. PDM provides anomaly detection and diagnosis of the potential printing failure cases to improve process quality and reduce downtime[5]. The experimental results show that the PDM can achieve more than 87% accuracy in predicting different types of defects, improper printer hardware issues in the board support, squeegee, paste conditions, etc. The CPC uses the SPI information to estimate residue buildup level on the stencil undersurface and assess the stencil cleaning profile and cycle control, as illustrated in Figure 4[6]. Upon the implementation of CPC, it is expected that the robustness of the printing quality and the Cpk can be improved by 34% and 10%, respectively, compared to the best-known cleaning parameters using in the production line. Figure 4. Smart residue buildup prediction for stencil cleaning operation control During the P P procedure, the mounter optimization module (MOM) and the mounter diagnosis module (MDM) can be applied as a machine's automation process while optimizing the P P machine's parameters. By utilizing self-alignment effects appropriately, MOM identifies the optimal placement position by predicting the component's post-reflow positions based on the data collected by SPI, Pre-AOI, and Post-AOI machines, as shown in Figure 2. MOM also offers the placement positions adaptively during active production. In the MOM framework, multiple dynamic placement options are first generated based on the solder paste offset information. The components' final offsets in both x and y directions are predicted by a hybrid AI model that stacks on the k-nearest neighbor regression and the gradient boosting regression models. The optimal placement, which has the minimum predicted post-reflow misalignment, can be identified by MOM. The experimental results show that MOM can decrease 18% of the final misalignments compared to a conventional P P placement method (i.e., placing a component on the pad center). MDM is a prescriptive and predictive maintenance method that uses P P machine operational and AOI inspection data to trace back the root causes of P P defects and prevent future failure. MDM can achieve an accuracy of 84.50% in identifying the known root causes of certain defects, such as improper nozzle size, parts' contamination, and feeder problem. It shows that different mounting defects can be detected and classified automatically when the abnormality is detected through AI-based diagnosis algorithms. Figure 5. The illustration of the optimal placement position in the MOM One of the reflow oven issues to be addressed is to find the optimal reflow oven temperature settings, which would affect the final quality of the PCB products. Solder paste manufacturers usually provide a target profile based on the solder paste composition's physical properties, and solder joint temperature is required to meet the given profile. Hence, reflow engineers should fine-tune reflow oven temperature manually to ensure a thermal profile outcome from the reflow oven to correctly meet a target profile, requiring substantial cost and effort. The research team proposes an automated reflow recipe optimization model based on the PCB thermal profile and its recipe. Figure 6. Optimized thermal recipe and thermal profile First, the initial recipe collects the data for the prediction model and identifies the relationship between the thermal profile and the corresponding recipe. Then, an AI-based model is developed to predict the thermal profile based on the input recipe. Compared to traditional methods, the AI-based method generates an optimal reflow oven recipe to minimize the gap between the predicted temperature and the given profile. As a result, the AI-based prediction model allows us to achieve promising results, such as 97% of fitness in the given profile temperature curve within one hour of processing time. The proposed model has other significant advantages, such as saving time, labor, and materials. It enhances the degree of automation of the PCB reflow process. In the future, data from multiple inspection machines will be integrated so that the reflow optimization process is fully automated and generates more reliable results. Summary and Conclusion The small-scale electronics products make the SMA processes much more complicated to maintain high-quality PCB products, and theoretical interpretations of the SMA processes can be challenging due to many uncertain factors. With the help of AI and big data collected from various inspectional operations, SMA processes can be intelligent and flexible in response to dynamic environmental situations. While retaining the optimal control parameters throughout the SMA processes, the final PCB product quality can be enhanced while maintaining the designed throughput. Automated and smart systems bring about the opportunity to next level of electronics manufacturing, which utilizes the data and information from the end-users through edge/cloud computing and fastens the customized product manufacturing with increasing efficiency for high-mix/low-volume manufacturing. Also, it can increase verities of design and fasten the delivery time. About the Author Prof. Sang Won Yoon is a recipient of the SUNY Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2019 and a highly successful researcher who leads many productive long-term industry collaborations. Prof. Yoon received his doctoral degree in School of Industrial Engineering at Purdue University, and he joined the faculty of the Watson School in the Department of Systems Science and Industrial Engineering at State University of New York at Binghamton in 2010. Prof. Yoon has been studying how to extract useful insights from expanding data sets to support intelligent decision-making processes. His research not only resides in better understanding large-scale data set by using statistical learning methodologies, but also leverages optimization, soft computing, simulation, and complex theories with conventional machine learning algorithms. As a result, Prof. Yoon has published in over 130 internationally renowned journals and conference proceedings. He was also a member of the Data Science Transdisciplinary Area of Excellence (TAE) initiative and is an active member of the Health Sciences TAE at his institution. The author recognizes the following for their assistance with this article: Daehan Won, [email protected], Assistant professor Jingxi He, [email protected], Ph.D. candidate Shrouq M. Alelaumi, [email protected], Ph.D. candidate Yuanyuan Li, [email protected], Ph.D. candidate Yuqiao Cen, [email protected], Ph.D. candidate References ​​​​​​​[1] Qi, Q., and Tao, F., 2018. Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, pp.3585-3593. [2] 10 Ways machine learning is revolutionizing manufacturing in 2019. https://www.forbes.com/sites/louiscolumbus/2019/08/11/10-ways-machine-learning-is-revolutionizing-manufacturing-in-2019/?sh=7cd2e9e22b40. [3] Khader, N. and Yoon, S.W., 2018. Stencil printing process optimization to control solder paste volume transfer efficiency. IEEE Transactions on Components, Packaging and Manufacturing Technology, 8(9), pp.1686-1694. [4] Lu, H., Wang, H., Yoon, S.W. and Won, D., 2019. Real-Time stencil printing optimization using a hybrid multi-layer online sequential extreme learning and evolutionary search approach. IEEE Transactions on Components, Packaging and Manufacturing Technology, 9(12), pp.2490-2498. [5] Alelaumi, S., Wang, H., Lu, H. and Yoon, S.W., 2020. A Predictive Abnormality Detection Model Using Ensemble Learning in Stencil Printing Process. IEEE Transactions on Components, Packaging and Manufacturing Technology, 10(9), pp.1560-1568. [6] Alelaumi, S., Khader, N., He, J., Lam, S. and Yoon, S.W., 2021. Residue buildup predictive modeling for stencil cleaning profile decision-making using recurrent neural network. Robotics and Computer-Integrated Manufacturing, 68, p.102041.

Connecting product development to manufacturing to the field in the semiconductor equipment industry by becoming a Model-Based Enterprise Manufacturers across many industries, including the semiconductor equipment industry, are facing pressure to dramatically reduce product development cycle and production ramp times while also enhancing product quality and reliability. This challenge is complicated when multiple configurations are deployed and then maintained, enhanced and upgraded in the field. Buzzwords like digital thread, digital twin, smart manufacturing, Industry 4.0, and digital supply networks point toward a fusion of process, technology, data, and talent that promise the game-changing outcomes needed to address these challenges. Yet, there remains uncertainty about how these various elements come together into a cohesive approach across the product lifecycle. One such approach involves establishing a Model-Based Enterprise (MBE), with alignment across organizational silos, processes, and technologies. Too often, however, an effort to transform to an MBE is frustrated by “random acts of digital” that pursue implementation of certain digital technologies but fall far short of delivering value. What is needed instead is a deeper understanding of the characteristics of a true MBE and the tools and approaches to transform the organization, processes, and data to an MBE and thrive in the marketplace. Elements of a Model-Based Enterprise Modeling in engineering is not new, but what’s emerging are MBEs that comprise digital models connected upstream and downstream over the entire product lifecycle, from a product’s conception and development through its production and end market installation and use. At the heart of such MBEs are digital threads—integrated sets of processes executed within an interconnected technology ecosystem that drive the end-to-end product lifecycle and provide MBE data traceability front to back. A digital thread in the MBE environment includes all of the process, data, and system capabilities that enable digital representations of the product lifecycle stages, or digital twins, of which there are three primary types: The product digital twin is a virtual or simulated representation of the product and each of its components and configurations. While most manufacturers manage engineering models with CAD/CAE solutions, just 15% use product digital twins. Leaders in this space have seized a competitive advantage in product development and accelerated engineering. Yet, becoming a true MBE requires more than product digital twins. The process digital twin is a model of the manufacturing equipment, processes, and the workforce required to carry out related operations. The process digital twin represents the operation of the physical factory floor and its assets, complete with workflows and instructions that describe how the manufacturing processes are performed. A process digital twin relies on data from the product digital twin and allows the enterprise to build according to a product plan and predict what may happen on the factory floor. About 5% of enterprises use process digital twins. After production, a service digital twin represents the installation, use, maintenance, and repair of each product operating in the end market. The service digital twin is informed by product and process digital twins to facilitate adjustments and enhancements based on real-world data. Less than 5% of manufacturers use service digital twins, which is perhaps expected due to their reliance on the existence of the product and process digital twins. When underlying data (e.g., models, specifications, and configurations) are standardized and integrated across a digital thread, an enterprise has the capacity to monitor and refine a product over the span of the thread while also injecting insights and improvements back into the thread. The result is that engineering and manufacturing and end customer usage feedback is continuous and efficient, achieving in weeks what once took months. Visibility into materials, costs, suppliers, and more enable the enterprise to pivot and keep production moving, even when dealing with unforeseen challenges. Real-time monitoring that synthesizes live data also helps reveal performance insights and end market issues (e.g., installation issues, quality issues, etc.) that allow improvements to the offerings and operations of the enterprise. While the manufacturing and product development benefits of an MBE may be clear, the path to becoming an MBE and achieving these benefits can be challenging. Figure 1. Representative MBE end-to-end digital thread that connects product development, manufacturing, and the field (Source: Deloitte Development LLC, 2021) Accelerating Transformation New and emerging manufacturers have an opportunity to build toward their MBE vision without the historic data constraints legacy systems can impose. For more established manufacturers, such is more typical in the semiconductor equipment space, legacy technology and processes can present obstacles to an MBE transformation path. Stakeholders who have invested time and resources implementing certain enterprise platforms (e.g., CAD, PLM, ALM, MES, ERP, etc.) often look for how these can be used to enable a digital thread and digital twins over the enterprise. This limiting view frustrates a broader, more holistic opportunity to transform to an MBE and thrive in the marketplace. There is thereby a dual imperative to define a modernized and scalable end-to-end technology architecture for managing the MBE product data while also establishing a capabilities implementation roadmap that rapidly leads to MBE maturity. To be successful in your MBE transformation, four core functional capabilities that enable a digital thread must be considered: digital engineering, industrial simulation, manufacturing execution, and real-time monitoring. Addressing each with optimized tools allows a manufacturer to rapidly move from strategy to reality. Recognizing the complexity of addressing these functional needs, Deloitte has developed preconfigured solutions to help expedite and enhance transformation across each of these core areas. Design with D-PLM Simulate with D-Sim Execute with D-MES Monitor with D-IoT Accelerates product and application lifecycle management transformations with a multi-phased approach, including a phased, multi-year PLM/ALM roadmap and business case. Facilitates the ability to test and refine processes in a virtual environment, rapidly revealing the most efficient and effective industrial processes more quickly than is possible in a real-world environment. Integrates pre-defined processes related to production planning, execution, tracking and tracing, quality management, data collection, and visualization, with integrations to PLM and ERP. Delivers fast implementation of IoT capabilities that connect, collect, and analyze a broad scope of production data to drive quicker returns for high-impact areas while cultivating digital adoption. While many enterprises have various initiatives in model-based systems and manufacturing, few have tied them together with an end-to-end digital thread and set of data standards over the entire product life cycle. The foregoing preconfigured solutions can help enterprises transform to a true MBE that can typically achieve: 15% – 20% better development efficiency 30% – 50% faster time to market 8% – 20% product cost reduction 10% – 30% cost of quality reduction With such improvement potential, this could be the right time to map out and accelerate your MBE transformation to support your evolving business models and products. Deloitte Consulting LLP Co-Authors Kevin Prendeville Principal, Product Strategy Lifecycle Management [email protected] Vijay Santhanam Managing Director, Product Strategy Lifecycle Management [email protected] Kenneth Norton Senior Manager, Product Strategy Lifecycle Management [email protected] Dan Hamling Specialist Master, Technology Semiconductor [email protected] As used in this document, “Deloitte” means Deloitte Consulting LLP, a subsidiary of Deloitte LLP. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte USA LLP, Deloitte LLP and their respective subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte shall not be responsible for any loss sustained by any person who relies on this publication. Copyright © 2021 Deloitte Development LLC. All rights reserved.

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.

Electric mobility, renewable energy and other technology innovations like IoT, 5G, smart manufacturing and robotics all require reliability, efficiency, and compact power systems, fueling the adoption of Silicon Carbide (SiC) and Gallium Nitride (GaN) to support lower voltages in significantly smaller devices. But chip designers must overcome the technological and economical challenges of integrating the two semiconductor materials into power systems.SEMI spoke with Elisabeth Brandl, Business Development Manager at EV Group about trends and new developments within the power electronics industry and the devices' application in smart mobility. Brandl shared her views ahead of her presentation at the SEMI SMART Mobility Forum, 18 February, as part of the SEMI Technology Unites Global Summit, 15-19 February 2021, online event. Join us to meet experts from EV Group and other key industry influencers. Registration is open. SEMI: What is driving new developments in power electronics?Brandl: Globally there are significant changes in infrastructure requirements for communication, automotive and power conversion. We need to look no further than the rising adoption of 5G, electric and hybrid vehicles, and renewable energy as examples of drivers of these changes. The device level, particularly in the field of power electronics, figures prominently in these shifts.The power electronics industry faces a growing number of scenarios where conventional silicon power devices are no longer suitable and are easily outperformed by new architectures mainly based on wide bandgap semiconductor materials like Silicon Carbide (SiC) and Gallium Nitride (GaN).SEMI: What industry challenges is power electronics innovation aiming to solve? Brandl: Power conversion efficiency is very important and needs further improvement as the related losses significantly contribute to the overall power consumption. For green power and a better environmental footprint, renewable energy is crucial, but so is overall power-consumption efficiency, yet the role of power devices is often underestimated. High-frequency and high-power applications, such as data center applications and inverters for renewable energy, where silicon power electronics are reaching their limits, are also important areas in power electronics.SEMI: How will the transition from silicon to compound semiconductor materials help?Brandl: The superior material properties of several compound semiconductors can tackle the need for lower losses in power conversion or better high-frequency behavior. Today, we mainly talk about GaN and SiC power devices as they are materials well-suited to address these needs. However, other materials like diamond and gallium oxide are in development for these applications. Material properties of SiC that enable thinner materials with lower power losses and better thermal behavior address power conversion efficiency as well as form factor challenges. GaN, especially in a high electron mobility transistor (HEMT), can be used for high-frequency applications.SEMI: What enables a better and more cost-effective manufacturability of SiC and GaN power devices?Brandl: For the end customer, a typical figure of merit regarding the cost effectiveness is $ per Ampere or Watt. While this seems simple, the reality is of course more complex. It is important to understand the main cost contributors within the manufacturing area. For SiC, this is clearly the substrate cost. In my presentation, I will show a way to reduce this cost via wafer bonding. For GaN, epitaxy – a method for growing or depositing mono crystalline films on a substrate – is the critical parameter. And of course, yield has a very big impact on cost effectiveness too, which means that good process control including metrology is very important.SEMI: Many semiconductor companies are already transitioning to silicon carbide and gallium nitride. Can you give us an example of a success story?Brandl: All the big power device manufacturers have either acquired or developed their SiC and/or GaN power device technology, so they also see a bright future for these wide bandgap semiconductors in the power device market. The most prominent success story is STMicroelectronics with its SiC MOSFET power devices, which have been implemented by Tesla in its Model 3 vehicles since 2018.SEMI: What is coming next?Brandl: New materials for power devices are being explored, such as diamond and gallium oxide. For SiC, the trend is moving toward 8-inch substrates, which is the focus of the funded EU project REACTION under the coordination of STMicroelectronics. Cost reduction and substrate availability also play a big role. All major power device manufacturers have contracts to secure the supply chain for SiC substrates because material availability is the main uncertainty at this time. Finally, collaborations along the supply chain are crucial and generally beneficial for all parties, as development requirements are better communicated and prioritized.Elisabeth Brandl is Business Development Manager at EV Group. She received her master in technical physics from the Johannes Kepler University Linz, Austria in Semiconductor and Solid State Physics. Since 2014, she has been responsible for Product Marketing Management for temporary bonding and compound semiconductors at EVG. The SMART Mobility Forum is the digital platform of SEMI Europe’s Global Automotive Advisory Council (GAAC) for industry stakeholders along the automotive and electronics value chains, from Design, Semiconductor Equipment and Materials Suppliers to Automotive OEMs.Smart Mobility is one of four SEMI initiatives focused on building communities, content, and activities around critical and emerging electronics markets. Read more about our Regional Chapters.Serena Brischetto is senior manager of Marketing and Communications at SEMI Europe.

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.

D-SIMLAB Technologies, a Singapore-based provider of simulation-based business analytics and optimisation software solutions, recently joined SEMI. I spoke with Peter Lendermann, the company’s co-founder and Chief Business Development Officer, about the company’s role in the smart manufacturing movement, how customers are benefiting from D-SIMLAB solutions, and what the future holds for smart manufacturing. Ng: What is D-SIMLAB’s mission?Lendermann: Our mission is to develop, market, and deliver high-performance simulation-based decision support solutions that enable corporations to enhance their performance in a sustainable manner leading to significant cost savings. In particular, we focus on semiconductor manufacturing material flow planning and optimisation but also do business in aviation where we help customers optimise their spare parts support operations. What these two domains have in common are three important attributes: They are capital intensive, their underlying operations are complex, and operations are also heavily affected by random, i.e. unpredictable events, which makes both planning and execution of manufacturing operations very challenging. D-SIMLAB is a spin-off from the Singapore Institute of Manufacturing Technology (SIMTech) under the Agency for Science Technology and Research (A*STAR). Our head office is in Silicon Island Singapore. We also have representations in Germany and the U.S. Most of our staff are industrial and computer engineers with up to 20 years of operations experience in their respective industry domain, as well as vast data analytics and software development capability.Ng: What solutions does D-SIMLAB offer to optimise semiconductor manufacturing?Lendermann: In the three-pillar smart manufacturing framework of Connect, Sense and Predict advocated by SEMI, our focus is on Predict though we emphasise the equal importance of the subsequent Act: Our solutions can Predict, for example, WIP waves or usage-based preventive maintenance due dates. But much more value-add can be realised once some decisions with regard to how to Act can be derived from such a prediction. The ability to pro-actively adjust action plans in a timely manner is essential to overcoming challenges arising from changing customer due dates, mix profile changes, untimely production line issues, and production capacity to be shared with R D lots effectively, so that ultimately our customers can enhance capacity, reduce cycle times and improve the due-date performance of their factories.To that end, our D-SIMCON solution suite spans the full spectrum of decision-support tools required to forecast, manage and optimise material flow – from operational scheduling and dispatching, WIP forecasting and dynamic and static capacity planning all the way to specific applications for fab load mix optimisation or for the enhancement of the product/layer dedication and resist allocation in the lithography area. Our solutions are implemented in numerous 6-, 8- and 12-inch wafer fabs operated by both IDMs and foundries worldwide with capacity ranging from 40,000 to 200,000 wafers per month.Ng: What are the key enablers of D-SIMLAB’s success?Lendermann: Our success lies in deploying production-ready solutions for our customers, allowing them to extract immediate value. Our solutions enable the portrayal of many domain-specific characteristics such as queue time constraints or specific equipment behaviour, which is absolutely essential to generating operationally feasible plans or schedules in order to be able to Act in the best possible manner according to what has been Predicted. Moreover, we have modules for automatic generation, calibration and maintenance of the underlying capacity model, including resolution of data inconsistencies as well as verification and validation of the model, to allow near real-time responses to continuously changing operations. And the associated optimisation approaches focus on creating maximum possible value with as few iterations as possible and within minimum time through smart heuristics and parallel computing infrastructure – a paradigm that is as powerful as it is cost-effective.Ng: What are a few of your more notable customer successes?Lendermann: As a result of the first implementation of our novel, multi-objective based Scheduler cum Dispatcher, a tool capacity gain of 8%, a transportation capacity gain of 10%, and an operator workload reduction of 25% were concurrently realised at one of the critical equipment groups in our customer’s fab. At another set of equipment groups in the same fab, a 7% increase of lots within the critical queue time limiting area was achieved.Another use case we successfully realised is fine-tuning of Preventive Maintenance plans: Based on a seven-day lot arrival forecast at each equipment generated with our WIP Forecaster, a recommendation is made when PM would be best possible without causing too much disruption in the WIP flow. The effect of this synchronisation of the PM plan with material flow enabled a dramatic reduction of the average queue lengths at critical equipment groups and the associated cycle times without incurring any capacity loss. Reduction of average queue length as a result of synchronising preventive maintenance with material flow. Ng: What challenges has D-SIMLAB been facing in the COVID-19 world?Lendermann: Obviously, software delivery projects have become more challenging for the time being since our engineers cannot be on-site frequently. But it also turned out that more and more services can be delivered remotely, which has the nice side effect of making the services more cost-effective for customers. Overall, we are confident that our solid customer base will enable us to sail steadily through these challenging times.Ng: Where does D-SIMLAB see the technological development heading?Lendermann: In the future, enriching decision support and manufacturing execution solutions with machine learning and other AI techniques will be critical in reducing dependency on human experience. This path is essential to making manufacturing operations fully Industry 4.0-compliant. D-SIMLAB will certainly be at the forefront of this development. Bee Bee Ng is president of SEMI Southeast Asia.

I recently spoke with Chan Pin CHONG, Executive Vice President and General Manager of Products and Solutions at Kulicke Soffa, about how smart manufacturing is driving new production efficiencies in the semiconductor industry. During our conversation, he also provided practical steps for factory operators to follow in evaluating their smart manufacturing needs in order to ensure successful implementation and discussed the potential payoffs. Based in Singapore, Kulicke Soffa is a leading global provider of ball bonding, advanced packaging, wedge bonding, and electronic assembly equipment for the semiconductor, power and automotive industries.Ng: Industry 4.0 and smart manufacturing are critical to the growth of the semiconductor industry. What does the smart manufacturing movement mean to you or Kulicke Soffa?Chong: The future of smart manufacturing is the vision of building a digital connected factory to drive new manufacturing efficiencies by combining physical and cyber technologies. Industry 4.0 integrates discrete systems and harnesses the power of large volumes of data to move towards greater automation.At K S, we define smart manufacturing across the following four key areas embedded in our roadmap for all K S products, from wire bonders and advance placement tools to pick and place machines: Interoperability – This is about machines, devices and sensors connecting to each other. In fact, the very basis of smart manufacturing is that all devices are connected. Information transparency – Through simulation, various artificial intelligence (AI) tools use contextual information to emulate the actual world. Technical assistance – Robots or machines support humans in making decisions or solving problems. Autonomous decision-making – This is our vision that robots or machines can learn from machines to make decisions on their own. Ng: Please elaborate on some of these areas and how they’re the relevant to smart manufacturing. Chong: The need for machines, devices and people to communicate with each other forms the basis of connectivity, the idea of all machines communicating with each other or a host. Connectivity protocols now in place for machine-to-machine connectivity include SEMA, SECS/GEM, SEMI-ELS and IPC-CFX. Machine technology uses various sensing technologies. For example, for a pick and place machine such as SMT platform on K S Hybrid, the algorithm to recognize and align processes is part of the sensor needed in each machine before to can process and add thousands of components to the substrate or panel. In a wire bonder, the ultrasonics or EFO signal can provide some form of sensing technology for a machine to detect process conditions. Importantly, these sensing technologies can be used to collect feedback for process improvements.One example of how K S machines are connected to the host is our use of an intermediate server or host named KNeXt to connects to all assembly equipment in the fab. The equipment can then, in turn, connect to an external secured cloud or K S Global Cloud.Ng: What are the objectives for smart manufacturing?Chong: The ultimate goal is to achieve higher factory productivity or better OEE (Overall Equipment Effectiveness) by improving machine yields, productivity and efficiency. The key is to leverage AI, 5G, the Internet of Things (Iot) and other industry 4.0 technologies to drive automation and process improvements. Ultimately, each factory must meet productivity, yield and cost goals. Smart manufacturing enables factory operators to meet these goals. That is the focus of smart manufacturing.Ng: What is the biggest potential benefit of smart manufacturing?Chong: Smart manufacturing uses data to predict outcomes of a process step or machine operation. Once data is available in the global cloud, analytics can start to build data sets to run statistical modelling and examine factory operation trends. We can also use the data to identify past machine behaviors in order anticipate outcomes, including undesirable ones that we can then prevent.In the SMT example, if we can systematically examine days or weeks of historical performance, we can plot some statistical variations in the process specifically to a pick or placer or a robot and anticipate or avoid problems. However, all sensors must be in place in the bond head or the robot so that we can detect changes or variations in the robot’s movements.Kulicke Soffa smart manufacturing facility Ng: What are some recent factory improvements smart manufacturing has enabled? Chong: Kulicke Soffa has contributed to the hierarchical architecture of the smart factory and key technologies. COVID-19 is driving demand for greater factory connectivity, and K S offers solutions that are key to remote management and full control of smart equipment from a central control and embedding Internet of Things (IoT), big data, cloud computing and sensors in manufacturing. Using these technologies, a small smart factory can be remotely operated and managed.With COVID-19 limiting air travel around the world and access to support engineers, the need has grown for remote machine access to reduce the downtime per machine. Remote factory access enables off-site engineers to remotely identify and diagnose machine problems.Ng: What are barriers to faster adoption of smart factories?Chong: While most smart factories are capable of network connectivity and data collection, a key challenge is the lack of a business model for smart factories and smart equipment. Most factories must justify major capital investments by demonstrating ROI (Return of investments) potential. Capital improvements for every factory usually take several years to implement and are based on a complex business model. Factory connectivity requires substantial investments and years to implement. The same is true of the cloud infrastructure buildouts necessary to generate big data and meaningful analytics. The executive mandate for factory management to install capability usually calls for specific business targets in the planning stage.Another longstanding barrier to entry is the lack of compatibility of existing tools with new factory protocols, raising the question of whether the cost of replacing legacy tools justifies the need for a smart factory. If new factory investment is required for the latest tools to support the production of new products, the ROI will be much easier to justify.Ng: How is AI is important in smart manufacturing?Chong: AI interprets and learn from data to perform tasks and meet specific goals. Good examples of AI implementations include Amazon’s Siri and Alexa voice-command devices and self-driving cars being developed by Google and Tesla.At K S, over the years we’ve implemented AI in our smart wire bonders to reduce human intervention in our ProCu-7, PSP-2, ProCu Loop 2, Pro Bump and overhang processes.Thanks to AI, with senses of signals from the bonder, we can reduce the amount of parameters that an engineer or technician have to do trial and error. With on bonder metrology, PBI, loop height, wire sway features, AI allows us to measure process efficiency and provide feedback.Over several years of AI development, we have leveraged the technology to monitor machines and provide real-time performance feedback in order to provide better closed loop control such as short tail recovery in our bonder process. We can also use the data to predict machine behavior, monitor its health and track maintenance. Ultimately, AI enables fabs to improve manufacturing efficiency, productivity, yields and device quality.Ng: What’s an example of how AI has solved your manufacturing equipment problems?Chong: We’ve used AI to set RPM (real time monitor) limits, identify defective P-parts and monitor various conditions such as wire size and capillaries. These types of cases can arise in any manufacturing environment as humans make process mistakes or use the wrong part for a machine. With AI, we can prevent these problems and reduce the risk of further material lost from the wire bonding process.Ng: What advice do you have for factories looking to implement smart manufacturing systems?Chong: To build a smart factory, start by focusing on a clear set of business objectives and how smart manufacturing will help minimize or eliminate current factory inefficiencies. In other words, start with the end in mind – the problems that needs to be solved and the business goals – and identify the information you need to demonstrate ROI. Do you need to resolve, automate or improve processes or just to be more efficient? Before investing millions or billions of dollars to build a smart factory, identify those clear goals upfront. Then map out the particulars of implementation to avoid major problems around standards, protocols and connectivity.Bee Bee Ng is president of SEMI Southeast Asia.

The costs of production are typically based on labor and materials and define manufacturing expenses. But is this approach accurate enough? What about the cost of poor quality and lack of efficiency in production? How is the pandemic impacting semiconductor manufacturing and what can we expect from the future?SEMI recently spoke with Dr. Eyal Kaufman, founder and CEO of QualityLine, a Kiryat Gat, Israel-based provider of smart manufacturing analytics solution, about manufacturing controls and how to select the best data source to improve product quality and yield. Kaufmann provided a snapshot of current best practices used by the company to improve manufacturing efficiencies and product quality while reducing costs. He also discussed the COVID-19 pandemic’s impact on semiconductor smart manufacturing and how artificial intelligence (AI) can help keep factory workers safe.For additional insights on smart manufacturing, join the virtual SEMI Global Smart Manufacturing Conference, October 20 - 22, 2020. Registration is open.SEMI: Real manufacturing costs are calculated based on different aspects such as failures in production, repairs, products returned, scrap of components or late deliveries. Lack of quality and efficiency in manufacturing can undermine a business. How are you helping businesses overcome these challenges?Kaufman: To increase profit margins, it is essential to identify inefficiencies and what improvements to prioritize. Once manufacturing quality and efficiency deficiencies have been measured, the next step is to continuously collect manufacturing data in order to run the final cost analysis and use the analytics to improve the manufacturing process.Smart manufacturing makes it possible to detect anomalies in automated factories, improve production performance and increase profitability. Today, automated data are collected from every machine and piece of test equipment in the factory. Still, manufacturing data collection in many industries remains manual and expensive because of the time and human resources involved. A real-time analytics system can automatically collect all data sources and select the relevant data for analysis, which today is the most accurate and effective way of measuring and resolving quality and efficiency deficiencies.Data-driven decisions made by smart manufacturing reduce costs and improve manufacturing strategies, enabling factory operators to increase product quality, drive higher production capacity and enhance product design for manufacturability. Analytics solutions monitor shop floor operations accessing vendors and subcontractors’ products criterion to run root cause analysis. All those data will reduce the return rate of faulty products and accelerate return on investment. This is why we definitely need smart manufacturing technologies!SEMI: Data accumulated during the manufacturing process includes vital information about failures, anomalies and machine usability. What data are necessary to create the best analytics solution?Kaufman: Many companies today run data mapping and automatic creation of data capture. They often wonder if they need to use testing data, sensors data or product design data, or whether they should collect feedback from their customers and vendors. The best way to create an effective manufacturing analytics system is to use data sources such as: Feedback from customers (returned units, customers complaints, etc..) Testing data from automated test equipment and manual test activities Feedback from technicians repairing faulty units Analysis of testing processes done by vendors Sensors data Data from our ERP/MES systems Artificial intelligence enables any type and size of data structure, even accumulated data, to be automatically integrated and interpreted. AI-based analytics can also establish correlations between each manufacturing stage to help factory operators quickly conduct deep diagnostic and root cause analysis for problem solving and prevention – all while leaving intact a factory’s existing process, machinery and data output. Machine learning evaluates how a factory runs its database and puts all the information generated into an analytics solution that provides the know-how to continuously improve factory efficiency.SEMI: How do you select the best data source to improve manufacturing quality and yield? Kaufman: The accuracy and integrity of data accumulated in our manufacturing process is key to controlling and improving yield and quality while reducing manufacturing costs. Smart manufacturing is a technology-driven approach that uses digital and remote connected machinery to monitor the production process. The goal is to identify anomalies in manufacturing processes and leverage analytics to improve process yield and product quality.To select the relevant data, we collect each type and source of data that can improve the efficiency of a real manufacturing cell: Test data from Automated Testing Equipment Test data from Manual Testing Processes Analyses of repairing processes (failed units during the manufacturing process and units that were returned from customers) Once the data structure is collected, the next step is to turn it into actionable information in the manufacturing process. QualityLine smart manufacturing solutions provide a complete one-stop solution to interpret any manufacturing data structure. Our advanced manufacturing analytics solution detects quality and yield anomalies to reveal production line inefficiencies and opportunities to improve manufacturing quality and efficiency.SEMI: How would you describe your approach?Kaufman: Industry 4.0 in manufacturing claims to be the fourth generation of the industrial revolution. Advanced technologies like manufacturing intelligence and machine learning can efficiently achieve zero defects on manufacturing lines. Digital factories leverage technologies and methodologies including: Big data Self-optimization Self-configuration Self-diagnosis Cognitive and machine learning Smart manufacturing technologies enhance the manufacturing process by continuously collecting and analyzing data in real-time to achieve and maintain high quality performance. The goal is to achieve a significant increase in efficiency and yield while reducing waste and inefficiency.Until now, there has been no viable way to integrate all saved manufacturing data into a unified database. QualityLine advanced manufacturing analytics make it possible for any factory to become digital without installing new hardware, which can be expensive and require not only the extensive integration of existing data but investments in training. Our user-friendly solution integrates manufacturing data for industries with zero automation by first collecting and analyzing data from any type of manual test procedure and then integrated it into manufacturing analytics to improve efficiency.SEMI: Why are Pass/Fail criteria insufficient for controlling manufacturing yield and quality?Kaufman: Managing a mass manufacturing process is always a challenge because hundreds of tasks must be successfully completed before products can ship to customers. At QualityLine, we establish a test process for each stage of the production flow, from the incoming raw material to the final stage prior to the delivery of finished goods to the client. To prevent unexpected downtime incidents, waste and defective products, we collect and interpret every type of relevant data and turn it into meaningful information, setting up the following capabilities: Collection and interpretation of test and process data of each single unit and from each process and plant Automatic detection of quality and yield problems Accurate and quick root cause analysis process Automatic alerts to abnormal issues Prediction process potential and level of failures Measurement of key performance indicators Many manufacturers base their test criteria of each parameter on one key indicator – Pass or Fail. If the test result shows a Pass, then the unit is ready to move on to the next manufacturing stage. If the test result shows Fail, then the unit is sent to a technician for further analysis.A simple Pass or Fail criteria for product quality is far from sufficient since it provides little or no information about edge cases, where one or more of the technical parameters of the unit under test is only within its allowed tolerance. Edge cases may lead to unit failure during operation such as in extreme environments (cold, heat, humidity, electrical overload, impact, etc.). In fact, when running a mass manufacturing line, it is impossible to continuously digest all the detailed information collected from testing stations. Data is analyzed in detail only when a critical quality problem emerges and further analysis is required to understand the root cause.Information overload and the disregard of important parameters makes it hard to control the process and improve quality and yield. New technologies make fast and scalable data integration possible so data can be collected in real time to detect quality issues early, identify complex process disruptions to avoid delivery delays and ensure the best possible product for customers. Only by accurately analyzing data as actionable information can factory operators control the manufacturing quality process.SEMI: How has COVID-19 impacted the smart manufacturing market? How has your technology helped factories remain online?Kaufman: Smart manufacturing is playing a significant role by helping manufacturers overcome COVID-19 challenges such as workforce reductions, social distancing, drops in sales for some specific products and extreme pressure to cut operational costs.Manufacturing leaders turned to us for a solution to the challenges of maintaining efficient factory operations with a limited workforce and reduced number of operating hours. Filling factory orders with fewer people on the floor is a struggle. Digital factory technologies enable remote monitoring of operations to increase efficiency and capacity. We are helping our clients improve efficiency while reducing costs. Our remote monitoring technology can provide the operational visibility to floor managers and engineering teams who cannot go physically to the factories due to safety restrictions. With our advanced manufacturing analytics, they have full end-to-end visibility and can remotely diagnose and solve production line issues. During this critical time, we are proud to be improving remote monitoring solutions to help the industry withstand the pandemic. Some of our clients would have closed their factories otherwise. We’ve been working to integrate manufacturing data in factories that were previously unautomated to drive high automation levels. Integrating processes with existing factory data, regardless of customer’s protocols or automation level, is our great technology advantage.SEMI: How will manufacturing and its supply chains look after COVID-19?Kaufman: Smart manufacturing is currently a necessity. We collect and analyze data not only to improve quality but to reduce client returns of faulty products by 50% and reduce waste by 22%, both critical points. Manufacturing challenges will continue to accelerate advancements in technology and improve efficiency, safety and productivity as more factory operators incorporate real-time data analytics and artificial intelligence (AI). SEMI: Will suppliers continue to explore new avenues for smart manufacturing technologies and what are their growth opportunities?Kaufman: Yes, definitely. The sector has already changed, with COVID-19 bringing both opportunities and challenges. Industry leaders are facing new pressure, with sudden materials shortages, drops in demand and worker unavailability. The growth opportunities for manufacturing are likely to be digital, as already evident in the immediate response to the crisis. Industry 4.0 solutions will be crucial to increase end-to-end supply-chain transparency, automation and data integration. QualityLine manufacturing analytics have improved key manufacturing performance metrics. For example, based on customer feedback, we’ve increased production yield by 30%, saving some of our customers millions of dollars. Improvements like this can help suppliers withstand pandemics.Dr. Eyal Kaufman, Founder and CEO at QualityLine, has senior management experience and over 25 years of expertise in business development, marketing, finance, operations, engineering and quality management at leading industrial companies. Prior to QualityLine, he served as VP of Mobileye, Cardo Systems, and Medisim Ltd., as well as CEO of OnTheGo Systems. Eyal holds a Ph.D. from California Intercontinental University, an MBA from City University of New York and a BSc. from the Technion in Israel.The SEMI SMART Manufacturing Initiative is a global effort to promote awareness and interest about smart manufacturing with focus on delivering industry-recognized best-in-class programs and services to enable members to maximize product quality, productivity and cost improvements through smart manufacturing. Activities are focused on building out core capabilities to enable smart manufacturing across the microelectronics supply chain.MADEin4 is a consortium of 47 partners from 10 countries connecting the full range of supply chain: from semiconductor equipment manufacturers and system-integrating metrology companies to RTOS and key applications such as the automotive industry. The MADEin4 Project develops next generation metrology tools, machine learning methods and applications in support of Industry 4.0 high volume manufacturing in the semiconductor manufacturing industry.Serena Brischetto is a senior manager of marketing and communications at SEMI Europe.

Teck Khiong, WOI, senior manager of Factory Integration at Infineon Technologies Asia Pacific Pte Ltd, recently shared with me how the Infineon backend plant in Singapore has benefited from its journey to qualify for the lighthouse certification.WOI is driving Infinion smart manufacturing projects with a strong focus in the area of connect and control using IoT (Internet of Things) and analytics technologies. Ng: How did the Infineon backend plant in Singapore distinguish itself to qualify for lighthouse certification? WOI: The Infineon Singapore backend manufacturing plant is proud to be a Lighthouse Certified Smart Manufacturing site as part of the World Economic Forum’s (WEF) Fourth Industrial Revolution platform. Our Industry 4.0 (I4.0) implementation reduces labor costs by 30% and improves capital efficiency by 15%. We drove this successful digital transformation continuously investing in our people development and digital backbone.Of the many initiatives under our I4.0 Smart Factory platform, five were selected for WEF Lighthouse submission and certification. Digital foundation with integrated connectivity and workflow execution We implemented an Internet of Things (IoT) framework to connect machines to manufacturing system more than two years ago. The digitization of our Work-in-Progress (WIP) management systems provides full traceability and gives us better control of the four Ms (Man-Machine-Method-Material). Material handling and process automation We progressively deployed automated solutions starting six years ago using autonomous transport, robotic material management systems and automation of packing processes. This eliminated non-value touches in areas of WIP storage and retrieval. Advanced algorithms enabled WIP scheduling and dispatching As our product mix and volume grew in complexity, our advanced algorithms has enabled us to increase our machine uptime, thus reducing idle and set-up time. Manufacturing control tower Our control tower provides a real-time pulse of the entire manufacturing process, from machine efficiency to quality. The tower also improves data integrity and collaborative information sharing while issuing early-warning alerts that enable exception management and timely decisions. Running a global virtual factory Our Global Production Network deployments allows us to connect and manage a growing contract-manufacturing network in real time, with the same transparency, traceability and control as if the manufacturers are our internal sites.About Teck Khiong, WOITeck Khiong, WOI graduated from Loughborough University in the UK with a Master of Science degree in Computer Integrated Manufacturing (CIM). For more than 20 years he has delivered manufacturing IT solutions to global backend (assembly and test) semiconductor manufacturing, ranging from equipment, factory, process control, material handling automation and manufacturing execution systems (MES).