Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)-Reference-Cited by-同舟云学术

Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)

Published:2021-10 Issue:4 Volume:30 Page:291-297
ISSN:1060-992X
Container-title:Optical Memory and Neural Networks
language:en
Short-container-title:Opt. Mem. Neural Networks

Author:

Tryasoguzov P. E.,Kuzovkov A. V.,Karandashev I. M.,Teplov G. S.

Publisher

Allerton Press

Subject

Electrical and Electronic Engineering,General Computer Science,Electronic, Optical and Magnetic Materials

Link

https://link.springer.com/content/pdf/10.3103/S1060992X21040056.pdf

Reference27 articles.

1. Otto, O.W. et al., Automated optical proximity correction: a rules-based approach, Optical/Laser Microlithography VII, Int. Society for Optics and Photonics, 1994, vol. 2197, pp. 278–293.

2. Li, J. et al., Model-based optical proximity correction including effects of photoresist processes, Optical Microlithography X, Int. Society for Optics and Photonics, 1997, vol. 3051, pp. 643–651.

3. Kolobov, A.V., Ivanov, V.V., Kuzovkov, A.V., and Arilin R.A., OPC model development for 65 nm technology node, Electron. Eng., Ser. 3: Microelectronics, 2016, no. 4 (164), pp. 4–9.

4. Cummings, K.D., Frye, R.C., and Rietman, E.A., Using a neural network to proximity correct patterns written with a Cambridge electron beam microfabricator 10.5 lithography system, Appl. Phys. Lett., 1990, vol. 57, no. 14, pp. 1431–1433.

5. Zach, F.X., Neural-network-based approach to resist modeling and opc, Optical Microlithography XVII, Int. Society for Optics and Photonics, 2004, vol. 5377, pp. 670–679.

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