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Proceedings Paper

Electron beam lithographic modeling assisted by artificial intelligence technology
Author(s): Noriaki Nakayamada; Rieko Nishimura; Satoru Miura; Haruyuki Nomura; Takashi Kamikubo
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Paper Abstract

We propose a new concept of tuning a point-spread function (a “kernel” function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.

Paper Details

Date Published: 13 July 2017
PDF: 7 pages
Proc. SPIE 10454, Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology, 104540B (13 July 2017); doi: 10.1117/12.2282841
Show Author Affiliations
Noriaki Nakayamada, NuFlare Technology, Inc. (Japan)
Rieko Nishimura, NuFlare Technology, Inc. (Japan)
Satoru Miura, NuFlare Technology, Inc. (Japan)
Haruyuki Nomura, NuFlare Technology, Inc. (Japan)
Takashi Kamikubo, NuFlare Technology, Inc. (Japan)


Published in SPIE Proceedings Vol. 10454:
Photomask Japan 2017: XXIV Symposium on Photomask and Next-Generation Lithography Mask Technology
Kiwamu Takehisa, Editor(s)

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