Investigation on attention-based convolutional architectures applied to optical proximity corrections

Albert Lin; Han-Chun Tung; Yen-Wei Feng; Peichen Yu

doi:10.1117/12.2676658

28 September 2023 Investigation on attention-based convolutional architectures applied to optical proximity corrections

Albert Lin, Han-Chun Tung, Yen-Wei Feng, Peichen Yu

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC126550M (2023) https://doi.org/10.1117/12.2676658
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

The optical proximity correction using machine learning has been a promising alternative to physical three-dimensional Maxwell solvers in recent years. The benefits are mainly reduced CPU runtime and the incorporation of the resist and etching phenomena that lack proper physical models. The network architecture has been a key to the accuracy of the machine learning model. The appropriate architecture should grasp the physics and essential features in mask-resist mapping so that the test set prediction is improved. In addition, the architecture also affects the training process where the fine mask feature should be fitted and reflected in the corresponding resist patterns. In this work, we use a modified Unet with attention (Ozan Oktay et al. MIDL 2018) to construct a machine-learning model for OPC. The modification is on the attention layers inserted to the place where the up-sampling and cropped skip connection are combined. Instead of solely using concatenation to combine the up-sampled and skip-connected data flow, self-attention mechanism is shown to be effective in increasing the prediction accuracy. The mask-to-resist pattern, the image-to-image dataset, is from the Canon FPA

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Albert Lin, Han-Chun Tung, Yen-Wei Feng, and Peichen Yu "Investigation on attention-based convolutional architectures applied to optical proximity corrections", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC126550M (28 September 2023); https://doi.org/10.1117/12.2676658

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KEYWORDS

Optical proximity correction

3D modeling

Convolution

Machine learning

Network architectures

Optical lithography

Photoresist processing

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