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Currently Playing: [Open DMQA Seminar] AI in Semiconductor Manufacturing

최근 학계와 빅테크기업을 중심으로 인공지능 기술의 급격한 성장과 혁신이 빠르게 진행되고 있다. 이와 함께 다양한 산업에서는 그 다음 단계로써, 인공지능의 활용 방안에 대해 집중하고 있다. 제조업 분야에서도 역시 다양한 방식으로 인공지능 도입을 시도하고 있다. 이번 세미나에서는 대표적인 제조업인 반도체 산업 분야에서 최근 연구되고 발표된 논문들을 통해, 인공지능을 어떤 분야에 적용하고 있는지 알아보고자 한다. 참고자료: [1] Plathottam, S. J., Rzonca, A., Lakhnori, R., & Iloeje, C. O. (2023). A review of artificial intelligence applications in manufacturing operations. Journal of Advanced Manufacturing and Processing, 5(3), e10159. [2] Ji, Y., & Chen, L. (2023). A fine-grained, end-to-end feature-scale CMP modeling paradigm based on fully convolutional neural networks. IEEE Transactions on Semiconductor Manufacturing, 36(2), 231-238. [3] Srichandan, S., Heider, F., Polak, Y., Ehrentraut, G., Juhasz, L., Haberjahn, M., ... & Haupt, R. (2023, May). Machine Learning for Deep Trench Bottom Width Measurements using Scatterometry: AM: Advanced Metrology. In 2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC) (pp. 1-6). IEEE. [4] Fan, S. K. S., Tsai, D. M., & Yeh, P. C. (2023). Effective variational-autoencoder-based generative models for highly imbalanced fault detection data in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 36(2), 205-214. [5] Kim, E., Kim, T., Lee, D., Kim, H., Kim, S., Kim, J., ... & Lee, T. E. (2023). Practical reinforcement learning for adaptive photolithography scheduler in mass production. IEEE Transactions on Semiconductor Manufacturing. [6] Hong, S., Hwang, I., & Jang, Y. J. (2022). Practical q-learning-based route-guidance and vehicle assignment for oht systems in semiconductor fabs. IEEE Transactions on Semiconductor Manufacturing, 35(3), 385-396. [7] Yuan, Z., & Wang, R. (2023). Multi-Scale and Multi-Branch Transformer Network for Remaining Useful Life Prediction in Ion Mill Etching Process. IEEE Transactions on Semiconductor Manufacturing. [8] An, D. W., Kim, S., Kim, H. K., & Kim, C. O. (2022). Commonality analysis for detecting failures caused by inspection tools in semiconductor manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 35(4), 596-604. [9] Lee, H., Lee, J., & Kim, H. (2023). Semi-supervised learning for simultaneous location detection and classification of mixed-type defect patterns in wafer bin maps. IEEE Transactions on Semiconductor Manufacturing, 36(2), 220-230. [10] Nam, Y., Joo, S., Kwak, N., Kim, K., & Kim, D. N. (2022). Precise pattern alignment for die-to-database inspection based on the generative adversarial network. IEEE Transactions on Semiconductor Manufacturing, 35(3), 532-539.