Binghamton University

The FLEX Conference, held again this year in conjunction with SEMICON West 2023, provided numerous examples of continued developments in flexible, printed, and flexible hybrid electronics technologies applied to sensing, robotics, communications, and other applications.

Focusing on applications that help assist movement in people with neuromotor disorders & aging muscle, Georgia Tech is developing a flexible system that uses sensory feedback through EMG to assess muscle and motor neuron health and augments strength through pneumatically actuated artificial muscle.

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.

New treatments for vascular disease. Optimized agricultural production. Beefed up performance of wearable devices and flexible displays. Four students with their sights set on making the world a better place won Innovators of the Future awards at the 20th Annual FLEX Conference in late February after presenting novel ideas for advancing flexible electronics in the popular student poster event. It was clear that all of these young innovators are working on projects with the potential to impact our lives in the near future. Their work is critical to advancing products, devices and basic research in flexible electronics. Posters created by the 17 students who competed for the awards were judged by a multidisciplinary panel of industry experts. The posters reflected a broad range of applications enabled by flexible hybrid devices and covered technology for wearables, medical devices and precision agriculture. Innovators of the Future Award Winners Robert Herbert from the Georgia Institute of Technology won first place for his paper Smart and Connected Stent System with Nanomembrane Soft Sensors for Wireless Monitoring of Hemodynamics. Vascular diseases are the leading cause of death worldwide, accounting for over 30% of all fatalities. Early diagnosis and monitoring blood pressure and flow rates are critical to effective treatment. Herbert’s poster introduced a less costly, less invasive and more revealing (spoiler alert) sensor system that uses a flexible, wireless biosensor system with an inductive medical stent and capacitive pressure sensors. The laser-machined stent uses multi-layered material integration to function as an inductive coil for wireless communication while maintaining mechanical properties similar to conventional vascular stents. The stent and sensor system can be easily deployed using conventional catheter procedures. Watch his presentation. Jose Waimin from Purdue University’s School of Materials Engineering was one of two second-place winners for his poster that shows how real-time monitoring of ion concentration, moisture, pH, microbial activity and other key metrics in agricultural production can optimize crop yields while reducing environmental impacts. His work presented a scalable alternative for manufacturing low-cost flexible sensors that can be used in an array of applications. Electrodes are manufactured in a Roll-to-Roll (R2R) process to enables fast production at a very low cost per device. Watch his talk. Benham Garakani from Binghamton University, Center for Advanced Microelectronics Manufacturing (CAMM) was the other second-place winner for his paper Electromechanical Behavior of Flexible Silver Paste and Highly Stretchable Liquid Metal for Wearable Electronics. Garakani explored how to improve fabrication of reliable, comfortable wearable devices to boost performance and functionality using substrates such as nonwoven high-density polyethylene fibers (HDPE) and thermoplastic polyurethane (TPU). Garakani also examined the electromechanical reliability of screen-printed silver trace on HDPE fibers and stencil-printed liquid metal (Ga-In-Sn alloy) on TPU during isothermal fatigue cycling. Watch his presentation. Sridhar Sivapurapu from the Georgia Institute of Technology won third place for his poster Flexible and Ultra-Thin 30µm Glass Substrates for RF and mmWave Flex Applications. Sivapurapu’s poster addressed the increasing demand for maximizing the mechanical flexibility of flexible displays while maintaining or improving their electrical performance. Sivapurapu focused on both electrical and mechanical properties for determining the viability of ultra-thin glass stack-ups for flexible RF applications by benchmarking the electrical performance of the ultra-thin glass stack-up to 110 GHz. He also examined electrical characterization during bending tests using free arc bending. Watch his talk. The Innovators of the Future award was sponsored by FlexEnable, a technology provider that develops flexible organic electronics technologies and OTFT materials. All FLEX Conference 2021 presentations are available through March 26, 2021 by registering for the event. Gity Samadi is co-chair of the FLEX Conference student poster awards and program manager at SEMI FlexTech.

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.

A short trip to Monterey, California provided an exciting glimpse into what is in store for the future. Along with 550 attendees and 60 exhibitors, I took a quick visit through the aisles and conference venue to find several exciting developments this year!So many exciting new products are on the horizon. Dr. Peter G. Hartwell, CTO of InvenSense, A TDK Group Company, provided a view future of the way sensors including optical, audio, balance, direction, location, and chemical will provide improvements over human capabilities. A glimpse into our future experiences with a 360-view winter wonderland experience of riding a snow mobile using two 180°C fisheye lens cameras with his presentation “Sensors: Where Reality Meets Virtual.” The only warning was that with so many cameras and social media privacy is lost!Dr. Hans Stork, CTO, ON Semiconductor discussed some of the recent investigations his company has made on the many LiDAR sensors. He enlightened listeners with more details of the optical/LiDAR Fusion with FUSE ONE that was unveiled at CES 2019. Future cars will have a combination of cameras, LiDAR, radar, and ultrasonics. No one sensor has it all. There are many companies offering LiDAR for automotive applications, but the products are still too expensive and the market will shake out over the next few years. Douglas Hackler, CEO, American Semiconductor presented the company’s achievement in flip chip on flex circuit assembly for a variety of applications, including pharmaceuticals, wearable wristbands, and IoT communications. Interconnects supported include ACA, ACF, advanced z-axis materials, and low temperature solder. He also described flexible hybrid electronics using printed electronics and a wafer CSP assembly for sensors. With this operation located in Idaho, products can be assembled in the U.S. Jean-Charles Souriau from CEA-Leti described the organization’s detailed research in developing in flip chip assembly on a flexible label with a thin die. A gold stud bump flip chip and thermo-compression bonding with glue is used to attach the die to a flex substrate. A polymer fabricated on thin glass was also demonstrated. Clearly, much progress has been made in flexible printed electronics in the last year with many presentations describing progress. Results of a benchmark study conducted at Cal Poly examined some of the key developments in bump materials and interconnect methods. Key areas such as antennas, batteries, PV and energy harvesting, a variety of sensors, and audio technology were investigated. Dr. Pradeep Lall presented work examining developments in conductive inks for 3D printed electronics.Dr. Subu Iyer and his student, Arsalan Alam, of UCLA presented some exciting research on heterogeneously integrated foldable display on elastomeric substrate, FlexTrate™, using vertically corrugated interconnects. This can be considered fan-out wafer level packaging. The work holds much promise for applications including foldable displays, wireless powered systems and surface electromyography systems. Fine pitch ≤40 micron interconnects bendable to 1 mm bending radius passed more than 6,000 bending cycles. Dr. Mark Poliks of Binghamton University described their work on the development of a wearable flexible hybrid electronics ECG monitor. While the work is in the early stages, human trials will soon begin and the results look promising. New materials will be key in the future products. Reliability test data was also presented on aerosol-jet printed traces on Upilex-S, including tensile, peel and bend testing, as well as “healing” of the damage. New product introductions included U.K’s Peratech’s EDGE force-sensing solution targeted form smartphones, wearables, and tablets. In this HMI solution, Peratech’s thin sensors are mechanically integrated into key areas of the smartphone to capture a user’s natural single-handed grip, ergonomic finger movements, intuitive pressure sand squeezes to control key functions. It even works with the users has wet hands or is wearing gloves! This eliminates the need for physical button openings and allows the implementation of a thinner, more contoured device with a rigid-metal chassis. Next year’s event will be in San Jose during the last week of February. Stay tuned to SEMI’s website for more details.Jan Vardaman is president and founder of TechSearch International, Inc., which has provided market research and technology trend analysis in semiconductor packaging since 1987. She is the co-author of How to Make IC Packages (by Nikkan Kogyo Shinbunsha), a columnist with Printed Circuit Design Fab/Circuits Assembly, and the author of numerous publications on emerging trends in semiconductor packaging and assembly. She is a senior member of IEEE EPS and is an IEEE EPS Distinguished Lecturer as well as a member of SEMI, SMTA, IMAPS, and MEPTEC.