Fast optical proximity correction based on graph convolution network

Shengen Zhang; Xu Ma; Junbi Zhang; Rui Chen; Yihua Pan; ChengZhen Yu; Lisong Dong; Yayi Wei; Gonzalo R. Arce

doi:10.1117/12.2583773

22 February 2021 Fast optical proximity correction based on graph convolution network

Shengen Zhang, Xu Ma, Junbi Zhang, Rui Chen, Yihua Pan, ChengZhen Yu, Lisong Dong, Yayi Wei, Gonzalo R. Arce

Proceedings Volume 11613, Optical Microlithography XXXIV; 116130V (2021) https://doi.org/10.1117/12.2583773
Event: SPIE Advanced Lithography, 2021, Online Only

Conference Poster

Abstract

Optical proximity correction (OPC) is regarded as one of the most important computational lithography approaches to improve the imaging performance of sub-wavelength lithography process. Traditional OPC methods are computationally intensive to pre-warp the mask pattern based on inverse optimization models. This paper develops a new kind of pixelated OPC method based on an emerging machine learning technique namely graph convolutional network (GCN) to improve the computational efficiency. In the proposed method, the target layout is raster-scanned into pixelated image, and the GCN is used to predict its corresponding OPC solution pixel by pixel. For each layout pixel, we first sub-sample its surrounding geometrical features using an incremental concentric circle sampling method. Then, these sampling points are converted into graph signals. Then, the GCN model is established to process the pre-defined graph signals and predict the central pixel within the sampling region on the OPC pattern. After that, the GCN is moved to predict the OPC solution of the next layout pixel. The proposed OPC method is validated and discussed based on a set of simulations, and is compared with traditional OPC methods.

Citation Download Citation

Shengen Zhang, Xu Ma, Junbi Zhang, Rui Chen, Yihua Pan, ChengZhen Yu, Lisong Dong, Yayi Wei, and Gonzalo R. Arce "Fast optical proximity correction based on graph convolution network", Proc. SPIE 11613, Optical Microlithography XXXIV, 116130V (22 February 2021); https://doi.org/10.1117/12.2583773

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