Pre-training CNN for fast EUV lithography simulation including M3D effects

Hiroyoshi Tanabe; Akira Jinguji; Atsushi Takahashi

doi:10.1117/12.3009880

10 April 2024 Pre-training CNN for fast EUV lithography simulation including M3D effects

Hiroyoshi Tanabe, Akira Jinguji, Atsushi Takahashi

Author Affiliations +

Proceedings Volume 12954, DTCO and Computational Patterning III; 129540I (2024) https://doi.org/10.1117/12.3009880
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Mask 3D (M3D) effects distort diffraction amplitudes from EUV masks. Electromagnetic (EM) simulations are used to rigorously calculate the distorted diffraction amplitudes. However, EM simulations are highly time consuming for OPC applications. The distorted diffraction amplitude can be characterized by M3D parameters. We develop a convolutional neural network (CNN) model which predicts M3D parameters very fast from input mask patterns. In this work, we train CNN using test mask data with various characteristics of metal layers. The accuracy of the CNN is good for the test mask data. However, when we use new mask data that mimic device patterns, the accuracy of the CNN is worsened. Starting from the CNN pre-trained by the test mask data, we improve the accuracy of the CNN by additional training using larger dataset including both the test mask data and the new mask data. The accuracy of the CNN is slightly improved by the fine tuning.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hiroyoshi Tanabe, Akira Jinguji, and Atsushi Takahashi "Pre-training CNN for fast EUV lithography simulation including M3D effects", Proc. SPIE 12954, DTCO and Computational Patterning III, 129540I (10 April 2024); https://doi.org/10.1117/12.3009880

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