Fast optical proximity correction based on graph convolution network

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.