PCB

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.

For many technologies, standards unshackle them from patents and enable their mass production – an idea close to the heart of Wendy Chen, associate vice president of the R D Center at King Yuan Electronics Corp. and vice chair of the SEMI Taiwan Test Committee. More importantly, standards are crucial to a product’s commercial success: Producing it in high volume reduces its price and helps drive widespread adoption.With standards part and parcel to the economies of manufacturing , SEMI has sought consensus over the years among key players in materials, equipment, and other manufacturing segments on the importance of standardization in a push to cut costs.Chen first set herself to work on SEMI standards development in 2010, when 74 percent of 3D IC patents were owned by IBM. At the time, SEMI saw the huge potential in 3D IC and believed the lack of technology standards might hamper the future of the semiconductor industry.Motivated by that conviction, SEMI established the 3DS-IC Standard Committee in the U.S. in July 2010 and the SEMI Taiwan 3DS-IC Standard Committee the following year, and before long the committees were working together to form standards targeting mass production at low cost. The Taiwan committee was co-chaired by Wendy Chen, Dr. Yi-shao Lai (Advanced Semiconductor Engineering), and Dr. Zhi-kun Gu (Industrial Technology Research Institute). The trio spearheaded 3DS-IC standard development efforts in Taiwan.In setting the 3DS-IC standards, SEMI put the needs of the manufacturing sector first, Chen says, to ensure their implementation throughout the supply chain. SEMI saw Taiwan’s development of 3D IC standards, coupled with its manufacturing prowess, as key to securing the region’s place in the global 3D IC market.Wide Range of Industries Prosper With SEMI StandardsOf course the influence of SEMI Standards extends well beyond 3D IC to include protocols for hardware and software communication, traceability, compound semiconductors, facilities, MEMS (micro-electromechanical systems), metrics, silicon wafers, carriers and automation systems. The standards are used in a broad range of manufacturing segments including panel display, photovoltaic, PCB and high brightness LED.As recently as last February, SEMI Taiwan formed a PCBECI (PCB equipment communication interface) equipment networking pilot team to build a solid foundation for smart PCB manufacturing in the region. The team combined the SECS (SEMI equipment communication standard) and GEM (generic equipment model) interfaces to create the PCBECI protocol.Security Standards Vital in Smart ManufacturingWith smart manufacturing’s aim to drive new efficiencies comes growing security concerns in the global microelectronics industry. Improving communication within a manufacturing facility, and between that facility and trusted suppliers or partners, is central to the success of smart manufacturing. To improve communications, the conduits for the flow of information must first be secure. SEMI Taiwan is answering this critical need by creating a task force to promote information security standards – an effort that will give Taiwan a powerful voice in the development of global standards.For Taiwan, SEMI Standards is the backbone of a thriving semiconductor manufacturing industry. As many as 25 SEMI Standards are cited in a purchase order for a piece of semiconductor processing equipment, and standards helped propel Taiwan’s rise as global semiconductor manufacturing power. The region has produced a staggering 2.2 billion wafers and 1.8 trillion IC devices.Taiwan on Track to Become World’s Largest Equipment MarketTaiwan’s semiconductor industry continues to gather strength. According to the SEMI 2019 Mid-Year Total Equipment Forecast, Taiwan will dethrone Korea as the largest equipment market and lead the world with 21.1 percent growth this year.Since Wendy Chen started her work on standards in 2010, SEMI has published about 200 protocols. As part of the SEMI Taiwan Test Committee, she joined the celebration for another milestone – the publication of the 1,000th SEMI International Standard in July. The corks of the champagne bottles popped nearly a half century after SEMI began developing standards to accelerate innovation and help power what today is the $2 trillion global electronics industry.And with Taiwan’s rise to the top of equipment market, it has good reason to cheer too. Emmy Yi is a marketing specialist at SEMI Taiwan.

Electronic EquipmentGlobal electronic equipment production is in its traditional “fall busy season” as the consumer driven end markets ramp up for the holiday season (Chart 1). Both normal seasonality and organic growth are driving this year’s upturn. September 2018 electronic equipment output was up almost 10% over the same month last year. This actual year-over-year growth when overlaid on an autumn seasonal upturn is providing a nice end of year finish. Source: Custer Consulting Group based on regional data American Electronic Supply ChainChart 2 shows the annualized (12/12) and 3-month (3/12) growth of the U.S./North American supply chain. Aside from the computer sector, all the domestic end markets are expanding driven by defense, electromedical, instruments and control equipment. Total domestic electronic equipment orders were up 8.2% in August 2018 versus August 2017.For components there are clear indications of slowing growth. Printed circuit board orders eased to a +2.7% expansion rate on a 3-month basis and passive component orders contracted 0.2%.The semiconductor industry appears to be coming down from its recent bubble as domestic SEMI capital equipment growth cooled to +3.8% and chip shipments to North America also slowed (to a still respectable) +15% on a 3-month (3/12) basis. Source: Custer Consulting Group based on U.S. Department of Commerce, IPC, SIA/WSTS and SEMI data Geographic ShiftsSemiconductors and semiconductor capital equipment shipments provide good insight into the changing center of gravity of world electronic production.Chart 3 shows semiconductor shipments to each region. This is not regional production but rather consumption -- an indication of regional demand. It effectively measures electronic assembly activity by area. In August over 62% of the world’s chip value was consumed in in Asia/Pacific with another 8.1% used in Japan. Europe consumed 8.5% and North America 21.9%. Chart 4 shows geographical shifts over time for semiconductor capital equipment. Although more volatile month-to-month than semiconductors, the SEMI Capex shift to Asia is obvious. Source: SIA and WSTS Walt Custer of Custer Consulting Group is an analyst focused on the global electronics industry.

Advances in areas from materials, packaging and testing to devices, equipment and standards must come together to enable smart factories and new capabilities in the microelectronics manufacturing supply chain. The secure transfer of information between factories is an important part of creating the “digital thread” that will give rise to higher yield, faster cycle times and reduced cost. At SEMICON West 2018, Tom Salmon, Vice President of Collaborative Technology Initiatives at SEMI, discusses his group’s work across the supply chain – including semiconductor front-end, packaging and test, as well as PCB and final product assembly – to accelerate the development of smart manufacturing. Ariana Raftopoulos is a marketing manager at SEMI.