A Review of Artificial Intelligence Techniques for Quality Control in Semiconductor Production
DOI:
https://doi.org/10.47941/ijce.1815Keywords:
Artificial Intelligence, Process Optimization, Quality Control, Semiconductor Production, Technological AdvancementsAbstract
Purpose: Exploring AI techniques to improve the quality control of semiconductor production brings numerous advantages, such as enhanced precision, heightened efficiency, and early detection of issues, cost reduction, continuous enhancement, and a competitive edge. These benefits establish this area of research and its practical application in the semiconductor industry as valuable and worthwhile.
Methodology: It aims to highlight the advancements, methodologies employed, and outcomes obtained thus far. By scrutinizing the current state of research, the primary objective of this paper is to identify significant challenges and issues associated with AI approaches in this domain. These challenges encompass data quality and availability, selecting appropriate algorithms, interpreting AI models, and integrating them with existing production systems. It is vital for researchers and industry professionals to understand these challenges to effectively address them and devise effective solutions. Moreover, it aims to lay the groundwork for future researchers, offering them a theoretical framework to devise potential solutions for enhancing quality control in semiconductor production. This review aims to drive a research on the semi-conductor production with the AI techniques to enhance the Quality control.
Findings: The main findings to offer research is more efficient and accurate approach compared to traditional manual methods, leading to improved product quality, reduced costs, and increased productivity. Armed with this knowledge, future researchers can design and implement innovative AI-driven solutions to enhance quality control in semiconductor production.
Unique contribution to theory, policy and practice: Overall, the theoretical foundation presented in this paper will aid researchers in developing novel solutions to improve quality control in the semiconductor industry, ultimately leading to enhanced product reliability and customer satisfaction.
Downloads
References
A. K. Gupta, A. Kumar, and N. K. Pande, "Machine learning assisted manufacturing," in Industry 4.0, ed: CRC Press, 2024, pp. 77-108.
K. Sawlani and A. Mesbah, "Perspectives on artificial intelligence for plasma-assisted manufacturing in semiconductor industry," in Artificial Intelligence in Manufacturing, ed: Elsevier, 2024, pp. 97-138.
M. Ismail, N. A. Mostafa, and A. El-Assal, "Quality monitoring in multistage manufacturing systems by using machine learning techniques," Journal of Intelligent Manufacturing, vol. 33, pp. 2471-2486, 2022.
S. M. Weiss, A. Dhurandhar, and R. J. Baseman, "Improving quality control by early prediction of manufacturing outcomes," in Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, 2013, pp. 1258-1266.
S. Y. Lee and M. J. Park, "Design of decision support system for yield management in semiconductor industry: application to artificial intelligence," International Journal of Business Information Systems, vol. 40, pp. 60-84, 2022.
D. V. Patel, R. Bonam, and A. A. Oberai, "Deep learning-based detection, classification, and localization of defects in semiconductor processes," Journal of Micro/nanolithography, MEMS, and MOEMS, vol. 19, pp. 024801-024801, 2020.
S. S. K. J. C. O. H. C. O. H. Moinuddin and C. STUDIES, "IMPACT OF DEMONETISATION: A SECTORAL ANALYSIS," 2019.
J. C. Pravin, J. P. Reddy, M. H. Chandra, and P. V. Reddy, "Optimization of Process Parameters for Semiconductor Devices using Artificial Neural Networks," in 2022 7th International Conference on Communication and Electronics Systems (ICCES), 2022, pp. 120-126.
M.-H. Oh, K. Lee, S. Kim, and B.-G. Park, "Data-driven multi-objective optimization with neural network-based sensitivity analysis for semiconductor devices," Engineering Applications of Artificial Intelligence, vol. 117, p. 105546, 2023.
A. Chazhoor, Y. Mounika, M. V. R. Sarobin, M. Sanjana, and R. Yasashvini, "Predictive maintenance using machine learning based classification models," in IOP Conference Series: Materials Science and Engineering, 2020, p. 012001.
A. Rammal, K. Ezukwoke, A. Hoayek, and M. Batton-Hubert, "Root cause prediction for failures in semiconductor industry, a genetic algorithm–machine learning approach," Scientific Reports, vol. 13, p. 4934, 2023.
B. Li, R.-S. Chen, and C.-Y. Liu, "Using intelligent technology and real-time feedback algorithm to improve manufacturing process in IoT semiconductor industry," The Journal of Supercomputing, vol. 77, pp. 4639-4658, 2021.
D. Jiang, W. Lin, and N. Raghavan, "A novel framework for semiconductor manufacturing final test yield classification using machine learning techniques," Ieee Access, vol. 8, pp. 197885-197895, 2020.
J. F. Arinez, Q. Chang, R. X. Gao, C. Xu, and J. Zhang, "Artificial intelligence in advanced manufacturing: Current status and future outlook," Journal of Manufacturing Science and Engineering, vol. 142, p. 110804, 2020.
J. A. McDermid, Y. Jia, Z. Porter, and I. Habli, "Artificial intelligence explainability: the technical and ethical dimensions," Philosophical Transactions of the Royal Society A, vol. 379, p. 20200363, 2021.
M. Huff, "Important Considerations Regarding Device Parameter Process Variations in Semiconductor-Based Manufacturing," ECS Journal of Solid State Science and Technology, vol. 10, p. 064002, 2021.
B. Li, R.-S. Chen, and C.-Y. Liu, "Using intelligent technology and real-time feedback algorithm to improve manufacturing process in IoT semiconductor industry," The Journal of Supercomputing, vol. 77, pp. 4639-4658, 2021.
A. Monteiro, C. Cepêda, A. C. F. Da Silva, and J. Vale, "The Relationship between AI Adoption Intensity and Internal Control System and Accounting Information Quality," Systems, vol. 11, p. 536, 2023.
A. Balahur, A. Jenet, I. T. Hupont, V. Charisi, A. Ganesh, C. B. Griesinger, et al., "Data quality requirements for inclusive, non-biased and trustworthy AI," 2022.
M. Javaid, A. Haleem, R. P. Singh, and R. Suman, "Artificial intelligence applications for industry 4.0: A literature-based study," Journal of Industrial Integration and Management, vol. 7, pp. 83-111, 2022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Rajat Suvra Das
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
