Classification method of lithographic layout patterns based on graph convolutional network with graph attention mechanism

Background: Layout classification is an important step in computational lithography approaches, such as the source-mask joint optimization, in which the representative samples are selected from each layout classification category to guide the source optimization. As an emerging machine learning method, graph convolutional network (GCN) can effectively perform the graph or image classification by defining a new propagation function to complete the convolution on the topological graph. Aim: We propose a new kind of GCN model combined with the graph attention mechanism, dubbed GAM-GCN, to classify the lithography layout patterns fast and accurately. Approach: By adding a graph attention layer, the weight coefficients of each pair of neighboring nodes are adaptively learned to improve the network performance. In addition, the model incorporates a skip connection structure to solve the oversmooth problem caused by the deep GCN model. Conclusions: Compared with some traditional deep learning methods and the GCN method, GAM-GCN obtains a significant improvement in classification accuracy while ensuring the computational efficiency.