Mr. Xianqiang Zheng Profile

Xianqiang Zheng

at Beijing Institute of Technology

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Proceedings Article | 22 February 2021 Poster + Paper

Lithography layout classification based on graph convolution network

Junbi Zhang, Xu Ma, Shengen Zhang, Xianqiang Zheng, Rui Chen, Yihua Pan, Lisong Dong, Yayi Wei, Gonzalo Arce

Proceedings Volume 11613, 116130U (2021) https://doi.org/10.1117/12.2583558

KEYWORDS: Lithography, Convolution, Signal processing, Network architectures, Image classification, Transform theory, Source mask optimization, Machine learning, Integrated circuits, Fourier transforms

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Layout classification is an important task used in lithography simulation approaches, such as source optimization (SO), source-mask joint optimization (SMO) and so on. In order to balance the performance and time consumption of optimization, it is necessary to classify a large number of cut layouts with the same key patterns. This paper proposes a new kind of classification method for lithography layout patterns based on graph convolution network (GCN). GCN is an emerging machine learning approach that achieves impressive performance in processing graph signals with nonEuclidean topology structures. The proposed method first transforms the layout patterns into graph signals, where the sum of several adjacent layout pixels is associated with one graph vertex. Next, the adjacent graph vertices are connected by the graph edges, where the edge weights are determined by the correlations between the vertices. Therefore, the layout geometries can be represented by the function values on the graph vertices and the adjacency matrix. Subsequently, the GCN framework is established based on the graph Fourier transform, where the input is the graph signal of the layout, and the output is its classification label. The network parameters of GCN are trained in a supervised manner. The proposed method is compared to the simple convolutional neural network (CNN) with a few layers and VGG-16 network, respectively. Finally, the features of different methods are discussed in terms of classification accuracy and computational efficiency.

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