Journal articles: 'Semiconductor Equipment Manufacturing'

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Wen, Yuan Lin, M. D. Jeng, and Yi Sheng Huang. "Diagnosability of Semiconductor Manufacturing Equipment." Materials Science Forum 505-507 (January 2006): 1135–40. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.1135.

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Katsuma, Takashi. "Vacuum manipulator for semiconductor manufacturing equipment." Industrial Robot: An International Journal 29, no. 4 (2002): 324–28. http://dx.doi.org/10.1108/01439910210441119.

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Munirathinam, Sathyan, and Balakrishnan Ramadoss. "Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process." International Journal of Engineering and Technology 8, no. 4 (2016): 273–85. http://dx.doi.org/10.7763/ijet.2016.v6.898.

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Munirathinam, Sathyan, and Balakrishnan Ramadoss. "Predictive Models for Equipment Fault Detection in the Semiconductor Manufacturing Process." International Journal of Engineering and Technology 8, no. 4 (2016): 273–85. http://dx.doi.org/10.7763/ijet.2016.v8.898.

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Saxena, S., and A. Unruh. "Diagnosis of semiconductor manufacturing equipment and processes." IEEE Transactions on Semiconductor Manufacturing 7, no. 2 (1994): 220–32. http://dx.doi.org/10.1109/66.286857.

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Subrahmanyam, Kommisetti, Scott Singlevich, Paul Ewing, and Michael Johnson. "Detecting Arcing Events in Semiconductor Manufacturing Equipment." IEEE Transactions on Semiconductor Manufacturing 26, no. 4 (2013): 488–92. http://dx.doi.org/10.1109/tsm.2013.2283053.

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Baudoin, C. R., and J. P. Kantor. "Software engineering for semiconductor manufacturing equipment suppliers." IEEE Transactions on Components, Packaging, and Manufacturing Technology: Part A 17, no. 2 (1994): 230–43. http://dx.doi.org/10.1109/95.296404.

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Ye, Jiahui, Ahmed El Desouky, and Alaa Elwany. "On the applications of additive manufacturing in semiconductor manufacturing equipment." Journal of Manufacturing Processes 124 (August 2024): 1065–79. http://dx.doi.org/10.1016/j.jmapro.2024.05.054.

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Park, Do-Joon, and Shuzhi Liu. "A Study on the Economic Effects of U.S. Export Controls on Semiconductors to China." Korea International Trade Research Institute 19, no. 1 (2023): 129–42. http://dx.doi.org/10.16980/jitc.19.1.202302.129.

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Purpose – This study addresses the development of China’s semiconductor industry in the context of the U.S.-China trade conflict, and analyzes the impact on other industries. Design/Methodology/Approach – Based on the multi-regional input-output table industry splitting method, the electrical and electronic equipment manufacturing industry in the Asian Development Bank’s multi-regional input-output table (ADB-MRIO, 2019) is split into semiconductor and non-semiconductor industries, and the impact of U.S. export controls on China’s semiconductor exports on domestic and foreign economies is simulated and analyzed using the hypothesis extraction and hypothesis expansion methods. Findings – The United States has suffered more than China from US export controls on semiconductors to China, and the impact of U.S. export controls on U.S. GDP decreasing by at most 0.0124‰, and China’s GDP decreasing by at most 0.00089‰. Since Japan, Korea, and European countries have become China’s semiconductor import substitutes, they all benefit from U.S. export controls on China. Second, the most affected industries in China are the chemical products, metal products, wholesale, financial, and non-semiconductor industries in the electrical and electronic equipment manufacturing industry. Research Implications – China should adopt coping strategies such as deepening international exchanges, enhancing communication between China and the U.S., and strengthening its scientific and technological strength.

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Espadinha-Cruz, Pedro, Radu Godina, and Eduardo M. G. Rodrigues. "A Review of Data Mining Applications in Semiconductor Manufacturing." Processes 9, no. 2 (2021): 305. http://dx.doi.org/10.3390/pr9020305.

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For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

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Parmar, Tarun. "Predictive Maintenance in Semiconductor Manufacturing: Leveraging IoT Sensor Data for Equipment Reliability." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem7616.

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Abstract—Predictive maintenance (PdM) has emerged as a transformative approach in semiconductor manufacturing, leveraging IoT sensor data to enhance equipment reliability and optimize production processes. This review explores the implementation of predictive maintenance strategies in semiconductor manufacturing, focusing on the role of IoT sensor networks, advanced analytics techniques, and integration with Manufacturing Execution Systems (MES). IoT sensor networks enable comprehensive real-time data collection on equipment performance and environmental conditions, thereby providing a foundation for predictive maintenance. Time series forecasting algorithms, such as ARIMA, exponential smoothing, and machine learning-based approaches, are employed to anticipate potential equipment failures. Anomaly detection techniques, including statistical methods and machine-learning algorithms, are used to identify unusual patterns or behaviors that are indicative of impending issues. The integration of predictive maintenance insights with MES allows for real-time decision making, process optimization, and improved overall equipment effectiveness. However, challenges persist in terms of data quality, scalability, and cybersecurity, requiring ongoing research and industry collaboration. Early adopters reported significant reductions in unplanned downtime, optimized maintenance schedules, and improved product quality. As the semiconductor industry continues to evolve, predictive maintenance is expected to play a crucial role in maintaining competitiveness and meeting the growing demand. Further research, standardization efforts, and development of best practices are essential to fully realize the potential of predictive maintenance in semiconductor manufacturing. Keywords—predictive maintenance (PdM), semiconductor manufacturing, IoT sensor data, equipment reliability, forecasting, anomaly detection, unplanned downtime, product quality

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Murdock, J. L., and B. Hayes-Roth. "Intelligent monitoring and control of semiconductor manufacturing equipment." IEEE Expert 6, no. 6 (1991): 19–31. http://dx.doi.org/10.1109/64.108948.

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Spanos, Costas J. "Creating and using equipment models in semiconductor manufacturing." Microelectronic Engineering 10, no. 3-4 (1991): 199–205. http://dx.doi.org/10.1016/0167-9317(91)90022-6.

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Peuse, Bruce. "Evolution of Commercial RTP Modules." Materials Science Forum 573-574 (March 2008): 21–31. http://dx.doi.org/10.4028/www.scientific.net/msf.573-574.21.

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Rapid Thermal Processing (RTP) has been a key technology for semiconductor manufacturing. The ability to rapidly change wafer-processing temperature in a well-controlled way is a distinguishing characteristic of RTP. Today’s state of the art single wafer RTP equipment is used for a wide range of thermal processes for the manufacturing of advanced semiconductor devices. Two different designs of halogen lamp based RTP equipment dominate the applications. The two equipment designs can be traced back to the early development of the semiconductor industry before there was wide acceptance of RTP. Two junctures in the evolutions of these designs resulted in the growth of RTP. The first juncture occurred when the conventional batch diffusion furnace could not satisfy some of the thermal budget and ambient control process requirements for semiconductor devices. A second juncture occurred with breakthrough developments in RTP equipment that enabled better control and repeatability of the process temperature. Developments of alternatives to tungsten halogen lamp based RTP will likely be seen in the future.

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Park, Hyoeun, Jeong Eun Choi, Dohyun Kim, and Sang Jeen Hong. "Artificial Immune System for Fault Detection and Classification of Semiconductor Equipment." Electronics 10, no. 8 (2021): 944. http://dx.doi.org/10.3390/electronics10080944.

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Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single misprocess could jeopardize the whole manufacturing process. In current manufacturing environments, data monitoring of equipment condition, wafer metrology, and inspection, etc., are used to probe any anomaly during the manufacturing process that could affect the final chip performance and quality. The purpose of investigation is fault detection and classification (FDC). Various methods, such as statistical or data mining methods with machine learning algorithms, have been employed for FDC. In this paper, we propose an artificial immune system (AIS), which is a biologically inspired computing algorithm, for FDC regarding semiconductor equipment. Process shifts caused by parts and modules aging over time are main processes of failure cause. We employ state variable identification (SVID) data, which contain current equipment operating condition, and optical emission spectroscopy (OES) data, which represent plasma process information obtained from faulty process scenario with intentional modification of the gas flow rate in a semiconductor fabrication process. We achieved a modeling prediction accuracy of modeling of 94.69% with selected SVID and OES and an accuracy of 93.68% with OES data alone. To conclude, the possibility of using an AIS in the field of semiconductor process decision making is proposed.

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Wang, Chien-Chih, and Yi-Ying Yang. "A Machine Learning Approach for Improving Wafer Acceptance Testing Based on an Analysis of Station and Equipment Combinations." Mathematics 11, no. 7 (2023): 1569. http://dx.doi.org/10.3390/math11071569.

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Semiconductor manufacturing is a complex and lengthy process. Even with their expertise and experience, engineers often cannot quickly identify anomalies in an extensive database. Most research into equipment combinations has focused on the manufacturing process’s efficiency, quality, and cost issues. There has been little consideration of the relationship between semiconductor station and equipment combinations and throughput. In this study, a machine learning approach that allows for the integration of control charts, clustering, and association rules were developed. This approach was used to identify equipment combinations that may harm production processes by analyzing the effect on Vt parameters of the equipment combinations used in wafer acceptance testing (WAT). The results showed that when the support is between 70% and 80% and the confidence level is 85%, it is possible to quickly select the specific combinations of 13 production stations that significantly impact the Vt values of all 39 production stations. Stations 046000 (EH308), 049200 (DW005), 049050 (DI303), and 060000 (DC393) were found to have the most abnormal equipment combinations. The results of this research will aid the detection of equipment errors during semiconductor manufacturing and assist the optimization of production scheduling.

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Hsu, Chia-Yu, Chen-Fu Chien, and Pei-Nong Chen. "Manufacturing intelligence for early warning of key equipment excursion for advanced equipment control in semiconductor manufacturing." Journal of the Chinese Institute of Industrial Engineers 29, no. 5 (2012): 303–13. http://dx.doi.org/10.1080/10170669.2012.702135.

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Parmar, Tarun. "Data-centric Approach to Decision Making in Semiconductor Manufacturing: Best Practices and Future Directions." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–6. https://doi.org/10.55041/ijsrem11025.

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Abstract—Data-centric approach to decision making has become increasingly crucial in semiconductor manufacturing, revolutionizing the industry's approach to efficiency, quality control, and cost reduction. The integration of advanced analytics, machine learning, and artificial intelligence enables real-time monitoring, predictive maintenance, and adaptive control systems, thereby minimizing downtime, reducing waste, and improving the overall equipment effectiveness. This study explores various types of data collected in semiconductor manufacturing, such as process parameters, equipment sensors, yield data, and quality metrics, and examines the role of advanced analytics techniques in extracting insights from these data. The importance of real-time data processing and analysis for rapid decision-making in semiconductor fabs is highlighted, along with the challenges of data quality, integration, and governance. The study also addresses the use of data visualization tools and techniques to present complex manufacturing data in an easily understandable format for decision-makers. Case studies of successful data-centric approaches in semiconductor manufacturing are examined, showing the benefits and lessons learned. The role of Industry 4.0 and the Internet of Things in enabling more comprehensive data collection and analysis is discussed, as well as the potential of edge computing and fog computing in processing data closer to the source. The integration of supply chain data with manufacturing data for more holistic decision making is explored, and the human factors in data-driven decision making, including the need for training and upskilling of the workforce, are addressed. Finally, the paper concludes with a discussion of future directions, including emerging technologies and trends that may shape data-centric decision-making in semiconductor manufacturing, such as advanced artificial intelligence, cellular networks, quantum computing, digital twins, and focus on sustainability and energy efficiency. Keywords— data-driven decision making, semiconductor manufacturing, advanced analytics, machine learning, artificial intelligence, predictive maintenance, adaptive control systems, overall equipment effectiveness (OEE), equipment sensors, yield data, quality metrics, real-time data processing, data quality and governance, data visualization

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Rostami, Hamideh, Jakey Blue, Argon Chen, and Claude Yugma. "Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing." International Journal of Intelligent Systems 36, no. 6 (2021): 2618–38. http://dx.doi.org/10.1002/int.22395.

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Bouaziz, M.-F., and E. Zamaï. "Equipment Health Factor prediction for complex semiconductor manufacturing facility." IFAC Proceedings Volumes 45, no. 6 (2012): 1005–10. http://dx.doi.org/10.3182/20120523-3-ro-2023.00377.

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Katari, Monish, Selvakumar Venkatasubbu, and Gowrisankar Krishnamoorthy. "Integration of Artificial Intelligence for Real-Time Fault Detection in Semiconductor Packaging." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 3 (2023): 473–95. http://dx.doi.org/10.60087/jklst.vol2.n3.p495.

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Semiconductor manufacturing involves a complex sequence of unit processes, where even a minor error can disrupt the entire production chain. Present-day manufacturing setups rely on continuous data monitoring of equipment health, wafer measurements, and inspections to identify any abnormalities that could impact the quality and performance of the final chip. The primary aim is fault detection and classification (FDC), for which a range of techniques including statistical analysis and machine learning algorithms are commonly employed. In this study, we introduce an innovative approach utilizing an artificial immune system (AIS), inspired by biological mechanisms, for FDC in semiconductor equipment. The main culprits behind process failures are shifts caused by the aging of parts and modules over time. Our methodology integrates state variable identification (SVID) data, reflecting current equipment conditions, and optical emission spectroscopy (OES) data, capturing plasma process information under faulty scenarios induced by deliberate gas flow rate adjustments in semiconductor fabrication. Our results demonstrate a modeling prediction accuracy of 94.69% when incorporating selected SVID and OES data, and 93.68% accuracy using OES data alone. In conclusion, we suggest the potential application of AIS in semiconductor process decision-making, offering promising avenues for enhancing fault detection in semiconductor equipment.

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Foong, Wai Yi, and Amir Hamzah bin Hassan. "Ergonomics in Semiconductor Wafer Manufacturing." Advanced Engineering Forum 10 (December 2013): 231–35. http://dx.doi.org/10.4028/www.scientific.net/aef.10.231.

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In semiconductor wafer manufacturing, there are only a few processes but many steps. Each wafer must go through the processes multiple times (steps) and sometimes not in the same sequence. All the wafers in lot size of 25 pieces are transferred between the processes using an Automated Guided Vehicle from stocker to stocker. Then, the wafers are manually transferred to the processing tool. Although the tools are designed per SEMI S2/S8 standards [1,, the equipment technician can get into awkward postures when performing the preventive maintenance. Both the manual material handling between the tools and awkward postures during preventive maintenance can pose an ergonomic challenge. However, some techniques can be used to minimize the impact. This paper shares the techniques which can ease ergonomic problems in semiconductor wafer manufacturing

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Hickey, Patrick, and Eugene Kozlovski. "E-strategies for aftermarket facilitation in the global semiconductor manufacturing industry." Journal of Enterprise Information Management 33, no. 3 (2020): 457–81. http://dx.doi.org/10.1108/jeim-05-2019-0124.

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PurposeThe paper presents one of the first attempts to identify and categorise the fundamental barriers currently preventing the multibillion semiconductor equipment manufacturing industry from implementing existing B2B e-trading models for its secondary market. It furthermore proposes a global e-business strategy supporting aftermarket integration with the industry's supply chain.Design/methodology/approachBecause of the global nature of the industry, the research employs a multiple case-study design to explore the state-of-the-art in semiconductor excess management. The data for this analysis are obtained through a number of in-depth interviews with experts from a cross-section of the industry, and further supplemented and validated with a systematic literature review and public corporate data.FindingsThe results indicate that significant market imperfections still exist in the industry due to information and knowledge deficits, organisational inefficiency and IP-related concerns. The considerable levels of third-party competition to the original equipment manufacturers raise questions about the existence and efficacy of reverse logistics processes and Closed-Loop Supply Chain (CLSC) management strategies in this industry. It has been shown that a leaner semiconductor supply chain is achievable through the implementation of the proposed B2B e-marketplace, maintaining the information exchange on the surplus/obsolete equipment and parts.Originality/valueThese outcomes are unique for supporting the design of the first global e-marketplace for the secondary semiconductor equipment and spares. The results can, furthermore, inform the standardisation of the semiconductor aftermarket transactions, streamline knowledge exchange mechanisms amongst different industry players and improve pricing strategies. These contribute to knowledge of principles allowing the aftermarket e-trading to become a key part of the value network in high-tech manufacturing industries.

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Park, You-Jin, and Sun Hur. "Improvement of Productivity through the Reduction of Unexpected Equipment Faults in Die Attach Equipment." Processes 8, no. 4 (2020): 394. http://dx.doi.org/10.3390/pr8040394.

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As one of the semiconductor back-end processes, die attach process is the process that attaches an individual non-defective die (or chip) produced from the semiconductor front-end production to the lead frame on a strip. With most other processes of semiconductor manufacturing, it is very important to improve productivity by lessening the occurrence of defective products generally represented as losses, and then find the fault causes which lower productivity of the die attach process. Thus, as the case study to analyze quantitatively the faults of the die attach process equipment, in this research, we developed analysis systems including statistical analysis functions to improve the productivity of die attach process. This research shows that the developed system can find the causes of equipment faults in die attach process equipment and help improve the productivity of the die attach process by controlling the critical parameters which cause unexpected equipment faults and losses.

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Tchatchoua, Philip, Guillaume Graton, Mustapha Ouladsine, and Jean-François Christaud. "Application of 1D ResNet for Multivariate Fault Detection on Semiconductor Manufacturing Equipment." Sensors 23, no. 22 (2023): 9099. http://dx.doi.org/10.3390/s23229099.

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Amid the ongoing emphasis on reducing manufacturing costs and enhancing productivity, one of the crucial objectives when manufacturing is to maintain process tools in optimal operating conditions. With advancements in sensing technologies, large amounts of data are collected during manufacturing processes, and the challenge today is to utilize these massive data efficiently. Some of these data are used for fault detection and classification (FDC) to evaluate the general condition of production machinery. The distinctive characteristics of semiconductor manufacturing, such as interdependent parameters, fluctuating behaviors over time, and frequently changing operating conditions, pose a major challenge in identifying defective wafers during the manufacturing process. To address this challenge, a multivariate fault detection method based on a 1D ResNet algorithm is introduced in this study. The aim is to identify anomalous wafers by analyzing the raw time-series data collected from multiple sensors throughout the semiconductor manufacturing process. To achieve this objective, a set of features is chosen from specified tools in the process chain to characterize the status of the wafers. Tests on the available data confirm that the gradient vanishing problem faced by very deep networks starts to occur with the plain 1D Convolutional Neural Network (CNN)-based method when the size of the network is deeper than 11 layers. To address this, a 1D Residual Network (ResNet)-based method is used. The experimental results show that the proposed method works more effectively and accurately compared to techniques using a plain 1D CNN and can thus be used for detecting abnormal wafers in the semiconductor manufacturing industry.

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Zhang, Ningjing. "Stock Prediction of TSMC and Intel using Machine Learning." BCP Business & Management 44 (April 27, 2023): 432–40. http://dx.doi.org/10.54691/bcpbm.v44i.4852.

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The advancement of communications, computers, healthcare, military systems, transportation, renewable energy, and numerous more uses is made possible by semiconductors, which are a crucial part of electronic equipment. Semiconductors have been experiencing a shortage due to various reasons. Thus, the increasing demand for chips has attracted countless people to invest in the semiconductor industry. Intel Corporation and Taiwan Semiconductor Manufacturing Company Limited (TSMC) are two companies that can be said to be dominating the industry currently as they have been making very advanced chips. As two giant suppliers in the semiconductor industry, they are close substitutes and competitors. This essay is going to evaluate Intel and TSMC s’ stocks for investors by using the method of machine learning to estimate and predict the future trend of the two stocks. The two models that will be used in the essay are the Linear model and the Long Short-Term Memory model (LSTM), showing the outcome of prediction and determining which stock is better for the investment.

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Cheng, Fan-Tien, and Chun-Yen Teng. "An object-based controller for equipment communications in semiconductor manufacturing." Robotics and Computer-Integrated Manufacturing 18, no. 5-6 (2002): 387–402. http://dx.doi.org/10.1016/s0736-5845(02)00028-5.

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Komoda, Kaori, Masashi Sakuma, Masakazu Yata, et al. "Measurement and Analysis of Seismic Response in Semiconductor Manufacturing Equipment." IEEE Transactions on Semiconductor Manufacturing 28, no. 3 (2015): 289–96. http://dx.doi.org/10.1109/tsm.2015.2427807.

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JAMAL, Dima EL, Bouchra ANANOU, Guillaume GRATON, Mustapha OULADSINE, and Jacques PINATON. "Remaining Useful Life Prediction of a Semiconductor Manufacturing Equipment Unit*." IFAC-PapersOnLine 56, no. 2 (2023): 11924–29. http://dx.doi.org/10.1016/j.ifacol.2023.10.607.

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Fujita, T. "Earthquake isolation technology for industrial facilities." Bulletin of the New Zealand Society for Earthquake Engineering 18, no. 3 (1985): 224–49. http://dx.doi.org/10.5459/bnzsee.18.3.224-249.

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In Japan earthquake isolation technology has become increasingly necessary with the growing dominance of so-called "high technology" industries and is now being used to provide effective aseismic protection for precision equipment which is of major importance in such countries. The present applications are for computer systems and semiconductor manufacturing equipment. For computers, particularly those of banks, earthquake isolation of the floors is widely used. For semiconductor manufacturing equipment, earthquake isolation has begun to be used for the components; the isolation of the floors will follow. For both applications, earthquake isolation has not yet been used for entire buildings, but is expected in the near future. Promising applications for the future include bio-technology facilities, highly automated factories and nuclear plants, especially fast breeder reactor power stations.

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Hung, Yu-Hsin. "Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process." Applied Sciences 11, no. 15 (2021): 6832. http://dx.doi.org/10.3390/app11156832.

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Industrial Internet of Things (IIoT) technologies comprise sensors, devices, networks, and applications from the edge to the cloud. Recent advances in data communication and application using IIoT have streamlined predictive maintenance (PdM) for equipment maintenance and quality management in manufacturing processes. PdM is useful in fields such as device, facility, and total quality management. PdM based on cloud or edge computing has revolutionized smart manufacturing processes. To address quality management problems, herein, we develop a new calculation method that improves ensemble-learning algorithms with adaptive learning to make a boosted decision tree more intelligent. The algorithm predicts main PdM issues, such as product failure or unqualified manufacturing equipment, in advance, thus improving the machine-learning performance. Herein, semiconductor and blister packing machine data are used separately in manufacturing data analytics. The former data help in predicting yield failure in a semiconductor manufacturing process. The blister packing machine data are used to predict the product packaging quality. Experimental results indicate that the proposed method is accurate, with an area under a receiver operating characteristic curve exceeding 96%. Thus, the proposed method provides a practical approach for PDM in semiconductor manufacturing processes and blister packing machines.

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Tang, Minghao. "Characteristics, application and development trend of the third-generation semiconductor." Applied and Computational Engineering 7, no. 1 (2023): 41–46. http://dx.doi.org/10.54254/2755-2721/7/20230337.

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Various devices made of the third-generation semiconductor have been gradually applied to various fields with the rapid development of the third-generation semiconductor materials equipment, manufacturing technology, and device physics represented by SiC and GaN. Firstly, the characteristics of the third-generation semiconductors is analyzed in this paper. Compared with the first-generation and second-generation semiconductors, the third-generation semiconductor has a wider band gap width, higher breakdown electric field, higher thermal conductivity, higher electron saturation rate and more expensive price. Then this paper will talk about the application of the third-generation semiconductor. The third-generation semiconductor materials can be mainly used in three fields, which are photoelectric, microwave radio frequency and power electronics. In terms of the photoelectric aspect, this paper takes the blue LED as an example. The blue LED is produced because of the wide band gap of the third-generation semiconductor. In the microwave RF aspect, the paper takes the 5G communication system as an example. Third-generation semiconductors make the high-frequency, high-power devices needed for 5G communications systems. In the power electronics aspect, the paper cites new energy vehicles as an example. Third-generation semiconductor components have a number of features needed for new-energy vehicles. For example, third-generation semiconductors can work at high temperatures. Finally, this paper will introduce the development trend of it. In the future, larger wafers will become mainstream. The third-generation semiconductors will be used in more fields. In addition, the new material systems will gradually mature.

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KINOSHITA, HIROO. "EUV LITHOGRAPHY FOR SEMICONDUCTOR MANUFACTURING AND NANOFABRICATION." COSMOS 03, no. 01 (2007): 51–77. http://dx.doi.org/10.1142/s0219607707000219.

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EUV lithography is the exposure technology in which even 15 nm node which is the limit of Si device can be achieved. Unlike the conventional optical lithography, this technology serves as a reflection type optical system, and a multilayer coated mirror is used. Development of manufacturing equipment is accelerated to aim at the utilization starting from 2011. The critical issues of development are the EUV light source which has the power over 115 W and resist with high sensitivity and low line edge roughness (LER).

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Nakos, Jim, and Joe Shepard. "The Expanding Role of Rapid Thermal Processing in CMOS Manufacturing." Materials Science Forum 573-574 (March 2008): 3–19. http://dx.doi.org/10.4028/www.scientific.net/msf.573-574.3.

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The role of single wafer Rapid Thermal Processing (RTP) in semiconductor manufacturing has been steadily expanding over the last 2 decades. There are several reasons for the successful adaptation of this technology. These include more critical requirements by advanced semiconductor technologies with respect to thermal exposure and control, as well as tremendous improvements by the RTP equipment community in resolving some fundamental limitations of the tooling, historically restricting wide spread implementation. From rather humble beginnings, RTP technology has now established itself as indispensable to the production of advanced semiconductor products. We review the history and implementation of RTP technology in semiconductor processing technology at International Business Machines Corporation (IBM) from the late 1980s to recent time.

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Рапницкий, М. Д. "The impact of semiconductor shortages in the COVID-19 pandemic on the Russian economy." Экономика и предпринимательство, no. 3(140) (June 17, 2022): 159–62. http://dx.doi.org/10.34925/eip.2022.140.03.027.

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Автопроизводство стало лишь одной из индустрий, пострадавших из-за нехватки чипов, изготовляемых из полупроводниковых материалов, необходимых для преобразования электрических сигналов. По сути, полупроводник - «мозг» любой электроники, от электрической зубной щетки и стиральной машины до современного медоборудования. Нехватка чипов также угрожает банкам - полупроводники нужны в том числе для производства дебетовых и кредитных карт. Automotive manufacturing has become just one of the industries affected by the shortage of chips made from semiconductor materials needed to convert electrical signals. In fact, a semiconductor is the "brain" of any electronics, from an electric toothbrush and washing machine to modern medical equipment. The shortage of chips also threatens banks - semiconductors are needed, among other things, for the production ofdebit and credit cards.

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Tengku Putri Nurain, Marwan Abdullah Hasan Al-Kubati, and Nur Azaliah Abu Bakar. "Enterprise Architecture for Equipment Performance Analysis Based on Internet of Things (IoT) Technology in the Semiconductor Manufacturing Industry." Open International Journal of Informatics 11, no. 1 (2023): 53–66. http://dx.doi.org/10.11113/oiji2023.11n1.247.

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This study proposes a concept for establishing an Enterprise Architecture in the semiconductor manufacturing industry for equipment performance analysis using Internet of Things (IoT) technology. The plan is to implement The Open Group Framework approach as Enterprise Architecture in the manufacturing analytics department. The TOGAF approach improves data governance in the business process and analytics department. Manufacturing focuses on providing excellent and high-quality products to customers. As a result, the manufacturer must use a data analytics application to monitor the performance of the equipment. The performance of the equipment is monitored around the clock to ensure that it meets the requirements and does not exceed the threshold. The business process, data from the warehouse, and how it is processed will be discussed. Implementing Enterprise Architecture in manufacturing will also be discussed, focusing on the three layers of the TOGAF Architecture Development Method (ADM). The three layers are business, technology, and application. The Enterprise Architecture framework is a blueprint for the architecture used to align the business and information technology. Enterprise architecture optimises business processes and structures processes and functions to integrate information technology into the business. The proposed Enterprise Architecture for equipment performance analysis in the semiconductor manufacturing industry can be used as a guideline for implementing a comprehensive framework for tool performance monitoring.

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Song, Tairan, Yan Qiao, Yunfang He, Jie Li, Naiqi Wu, and Bin Liu. "A New Framework of the EAP System in Semiconductor Manufacturing Internet of Things." Electronics 12, no. 18 (2023): 3910. http://dx.doi.org/10.3390/electronics12183910.

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In modern semiconductor manufacturing, the computer-integrated manufacturing system plays an essential role in automation with plenty of software systems. Among them, the equipment automation program (EAP) is one of the fundamental systems to support the interconnection of various types of equipment. For the traditional EAP, the communication and logic models are tightly coupled. The occurrence of any exception in EAP may make the EAP power down such that no equipment is reachable. Additionally, it can handle a couple of manufacturing tools only. The extension of manufacturing tools in a semiconductor fab makes the investment in EAP unbearable. Thus, fabs are highly desired to solve such problems of the traditional EAP. To do so, this work designs a new framework for a distributed EAP system with new technologies being adopted to enhance the usage and stability of EAP. Additionally, this design philosophy makes the distributed EAP system more compatible and expansible. Further, this EAP system can be upgraded as communication and big data technologies advance. Experiments are carried out to verify the stability of the designed distributed EAP system.

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Wang, Chen Xi, Ming Zhe Liu, and Ai Dong Xu. "Implementation of a Customiazble GUI Software Platform for IC Equipment." Applied Mechanics and Materials 568-570 (June 2014): 1455–58. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.1455.

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With the development of semiconductor industry, different types of wafer processing are increasing. According to the different wafer processing models, the need of data display and process is different. In this paper, a customizable software platform is described for the manufacturing equipment of semiconductor integrated circuit (IC equipment). The C# control technology has been used to build the IC equipment customizable control system interface. The development method of customizable control technology based on C# can realize the control of the reuse and codes sharing, in order to improve programming efficiency, avoid the development of two times, cost saving and be easy to debug.

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Mević, Amina. "Human Interpretable Virtual Metrology in the Semiconductor Manufacturing." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (2025): 29283–84. https://doi.org/10.1609/aaai.v39i28.35219.

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My PhD research focuses on developing a highly accurate and explainable multi-output virtual metrology system for semiconductor manufacturing. Using machine learning, we predict the physical properties of metal layers from process parameters captured by production equipment sensors. Key contributions include a model-agnostic explanatory method based on projective operators, providing insights into the most influential features for multi-output predictions and feature selection algorithms for these tasks.

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Jang, Seok-Woo, and Gye-Young Kim. "A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis." International Journal of Distributed Sensor Networks 13, no. 7 (2017): 155014771772181. http://dx.doi.org/10.1177/1550147717721810.

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This article proposes an intelligent monitoring system for semiconductor manufacturing equipment, which determines spec-in or spec-out for a wafer in process, using Internet of Things–based big data analysis. The proposed system consists of three phases: initialization, learning, and prediction in real time. The initialization sets the weights and the effective steps for all parameters of equipment to be monitored. The learning performs a clustering to assign similar patterns to the same class. The patterns consist of a multiple time-series produced by semiconductor manufacturing equipment and an after clean inspection measured by the corresponding tester. We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. The modified Line, Buzo, and Gray algorithm outputs a reference model for every cluster. The prediction compares a time-series entered in real time with the reference model using statistical dynamic time warping to find the best matched pattern and then calculates a predicted after clean inspection by combining the measured after clean inspection, the dissimilarity, and the weights. Finally, it determines spec-in or spec-out for the wafer. We will present experimental results that show how the proposed system is applied on the data acquired from semiconductor etching equipment.

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A. Rida, Jafaar Fahad. "The Employment of Carbon Nanotubes in Biomedical Applications." Scalable Computing: Practice and Experience 25, no. 5 (2024): 4283–300. http://dx.doi.org/10.12694/scpe.v25i5.3042.

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Carbon nanotubes (CNTs), a prominent application of nanotechnology, find extensive use across various fields. Their electrical and optical characteristics, which are affected by the manufacturing process and any impurities introduced during production, are crucial in establishing their suitability for use. This research focuses on the utilization of carbon nanotubes in medical applications, exploring their properties both as electrical conductors and semiconductors, comparable to silicon used in precision medical equipment and devices. When functioning as electrical conductors, CNTs exhibit characteristics similar to traditional conductive materials. This property is harnessed in medical applications, particularly in targeted cancer treatments that minimize impact on healthy cells. CNTs’ efficient conduction of electrical current makes them valuable components in medical devices and equipment. Furthermore, CNTs showcase semiconductor properties akin to silicon. This characteristic is crucial for developing advanced medical equipment, enabling accurate diagnostics and medical imaging. The semiconductor behavior allows the creation of intricate medical devices with enhanced precision. The research underscores the significance of CNTs in shaping the future of medical technology, especially when integrated with artificial intelligence applications. The ability of CNTs to function both as conductors and semiconductors highlights their versatility in the medical field, promising advancements in healthcare technologies. Their use holds potential for targeted cancer treatments, accurate diagnostics, medical imaging, and enhanced performance through integration with artificial intelligence.

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Jiang, Tao, and Huiyong Hu. "Review of Evolution and Rising Significance of Wafer-Level Electroplating Equipment in Semiconductor Manufacturing." Electronics 14, no. 5 (2025): 894. https://doi.org/10.3390/electronics14050894.

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Electroplating has become a cornerstone technology in semiconductor manufacturing, enabling high-performance interconnects and advanced packaging. Since the introduction of the Damascene Cu process at the 180 nm node, it has evolved to meet the demands for precision, uniformity, and scalability in miniaturized nodes and complex packaging architectures. The shift to horizontal electroplating systems has enhanced uniformity and process stability, particularly for applications such as TSVs, Cu pillars, micro-bumps, and RDLs. Emerging innovations like pulse electroplating, segmented anode control, and AI-driven monitoring are addressing the challenges of fine-pitch interconnects and emerging interconnect materials, such as cobalt. These advancements are critical for high-density interconnects used in AI, HPC, and high-frequency applications. This review explores the advancements in electroplating technologies, focusing on their role in semiconductor manufacturing. It highlights the evolving equipment designs and their implications for achieving precision, scalability, and reliability at advanced nodes. The ongoing development of electroplating equipment and techniques will support the reliability and performance of future semiconductor devices, reinforcing electroplating as a cornerstone technology in advanced packaging and fabrication.

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HAYAMA, OSAMU. "Chronological overview and future trends in semiconductor and manufacturing equipment industries." Journal of the Japan Society of Precision Engineering 51, no. 12 (1985): 2172–76. http://dx.doi.org/10.2493/jjspe1933.51.2172.

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Cho, Yoon Sang, Sang Min Lee, Hae Joong Kim, and Seoung Bum Kim. "Detection of Faulty Equipment Sequence of Multivariate Processes in Semiconductor Manufacturing." Journal of the Korean Institute of Industrial Engineers 44, no. 5 (2018): 325–33. http://dx.doi.org/10.7232/jkiie.2018.44.5.325.

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SASAKI, Ichiro, Hiroyuki KAWAKAMI, Masashi FUKAYA, et al. "G0402 Particle adhering Reduction by Flow Analysis on Semiconductor Manufacturing Equipment." Proceedings of the Fluids engineering conference 2013 (2013): _G0402–01_—_G0402–02_. http://dx.doi.org/10.1299/jsmefed.2013._g0402-01_.

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Lee, Jay, Huwei Dong, Dai-Yan Ji, and Pradeep Kundu. "Cyber-Physical Systems Framework for Predictive Metrology in Semiconductor Manufacturing Process." International Journal of Precision Engineering and Manufacturing-Smart Technology 1, no. 1 (2023): 107–13. http://dx.doi.org/10.57062/ijpem-st.2022.0010.

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The process of semiconductor manufacturing is very complex, and the downtime caused by equipment degradation at any process stage reduces yield and production efficiency. Thus, semiconductor machines’ maintenance and calibration are important in maintaining stable production output and yield improvement. Traditionally, semiconductor manufacturers emphasize the importance of product quality to distinguish product defects. However, as semiconductor reaches nanometer-level precision, monitoring process quality is necessary to control process variability and achieve optimal yields. This paper presented a 5-level Cyber Physical Systems (CPS) predictive metrology architecture to investigate the process quality. The proposed architecture performs a peer-to-peer comparison to satisfy self-comparison ability, which is critical for chamber-to-chamber matching in the semiconductor manufacturing process. This concept has been demonstrated using the 2016 PHM public dataset in which the material removal rate was predicted in the chemical-mechanical planarization process. The results show that this innovative 5-level CPS predictive metrology framework is promising and feasible.

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Katari, Monish. "The Impact of Extreme Ultraviolet Lithography (EUVL) on Semiconductor Scaling." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 2, no. 1 (2024): 248–61. http://dx.doi.org/10.60087/jaigs.v2i1.190.

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Extreme Ultraviolet Lithography (EUVL) represents a significant advancement in semiconductor manufacturing, enabling further scaling down of device features beyond the limits of traditional photolithography. This paper explores the impact of EUVL on semiconductor scaling, detailing its technical principles, advantages, and challenges. EUVL facilitates the production of smaller, more efficient, and powerful semiconductor devices by using a shorter wavelength of light (13.5 nm) compared to deep ultraviolet lithography. This technology allows for finer patterning, reducing feature sizes to below 7 nm, thus supporting the continuation of Moore's Law. However, the implementation of EUVL comes with its own set of challenges, including high equipment costs, complex process integration, and the need for specialized materials and masks. The paper discusses the current state of EUVL technology, its integration into semiconductor manufacturing, and future prospects in the context of ongoing advancements in semiconductor scaling.

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Tu, Ying Mei. "Throughput Estimation Model of Cluster Tool in Semiconductor Manufacturing." Key Engineering Materials 814 (July 2019): 196–202. http://dx.doi.org/10.4028/www.scientific.net/kem.814.196.

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Semiconductor manufacturing management system was developed and grown up over the past decades. In order to increase the product yield and enhance the production productivity, cluster tools became the main stream in modern wafer fabrication factories which occupies over 50% of production equipment. Generally, cluster tools are integrated by several components including robots, vacuum chambers (Load locks) and single-wafer process chambers in a module and can be treated as a small factory. The throughput estimation before recipe release is very difficult. However, it is necessary and important for the planning activity. In this work, a throughput estimation model for cluster tools is proposed. The Multiple Regression Analysis is applied to develop a set of throughput estimation equations. A simulation model of cluster equipment including 3 single-wafer process chambers are built to get the historical throughput data for the regression analysis. From the Multiple Regression Analysis, it reveals that different numbers of recipes processed in the same time have to develop different regression model. The major factors in the regression model include numbers of load ports and process time of each recipe. Furthermore, a set of recipes are used to test the accuracy of estimation. Based on the testing results, they revealed that the MAPE is under 3% and the estimation model is accepted in practice to forecast the throughput of recipes for the planning activities.

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Anthony Vincent, Darin Moreira, and Bhuvenesh Rajamony. "Algorithm to Improve Process Robustness in the Assembly & Test Manufacturing Industry A Case Study of the 1064nm Wavelength Laser Mark Equipment." Applied Mechanics and Materials 421 (September 2013): 898–903. http://dx.doi.org/10.4028/www.scientific.net/amm.421.898.

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This document explains and demonstrates the generic improvement algorithm created to enhance the maintenance methodology in the semiconductor manufacturing environment. The use of this algorithm demonstrates how a process and equipment can utilize it and get better output quality from a process and cost standpoint, which is a key driver in any manufacturing industry.

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MAEDA, Kazuo. "Vacuum Equipment and Application for Thin Film in the Multimedia. Vacuum Equipment in Semiconductor Device Manufacturing." Journal of the Surface Finishing Society of Japan 48, no. 11 (1997): 1043–49. http://dx.doi.org/10.4139/sfj.48.1043.

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Journal articles on the topic 'Semiconductor Equipment Manufacturing'

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