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

Junbi Zhang; Xu Ma; Shengen Zhang

doi:10.1117/1.JMM.22.3.034202

7 September 2023 Classification method of lithographic layout patterns based on graph convolutional network with graph attention mechanism

Junbi Zhang, Xu Ma, Shengen Zhang

Author Affiliations +

Journal of Micro/Nanopatterning, Materials, and Metrology, Vol. 22, Issue 3, 034202 (September 2023). https://doi.org/10.1117/1.JMM.22.3.034202

Abstract

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 over-smooth 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.

Citation Download Citation

Junbi Zhang, Xu Ma, and Shengen Zhang "Classification method of lithographic layout patterns based on graph convolutional network with graph attention mechanism," Journal of Micro/Nanopatterning, Materials, and Metrology 22(3), 034202 (7 September 2023). https://doi.org/10.1117/1.JMM.22.3.034202

Received: 28 December 2022; Accepted: 23 August 2023; Published: 7 September 2023

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