A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Author:

Huang An Chi,Meng Sheng Hui,Huang Tian Jiun

Funder

National Key R&D Program of China

SZSTC Project

Shenzhen Key Laboratory

High-Level Talents Project

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Reference242 articles.

1. Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., Tzovaras, D.: Machine learning and deep learning in smart manufacturing: the smart grid paradigm. Comput. Sci. Rev. 1(40), 100341 (2021)

2. Cheng, Q., Kwon, J., Glick, M., Bahadori, M., Carloni, L.P., Bergman, K.: Silicon photonics codesign for deep learning. Proc. IEEE. 108(8), 1261–1282 (2020)

3. Theis, T.N., Wong, H.S.: The end of Moore’s law: a new beginning for information technology. Comput. Sci. Eng. 19(2), 41–50 (2017)

4. Semiconductor industry association, state of the U.S. semiconductor industry. https://www.semiconductors.org/state-of-the-u-s-semiconductor-industry/ (2021)

5. Takiguchi, T., Takarada, Y., Fukada, T., Sugiyama, S., Yoshimura, K.: Lithography tool improvement at productivity and performance with data analysis and machine learning. InPhotomask Technology, vol. 11855, pp. 98–106. SPIE (2021)

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