Fast Optical Proximity Correction Using Graph Convolutional Network With Autoencoders

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OPC is a very time consuming process for mask synthesis. Quick and accurate OPC using GCN with layout encoder and mask decoder is proposed. (1) GCN performs a series of aggregation with MLP for correction process. A feature of a particular polygon is aggregated with weighted features of neighbor polygons; this is a key motivation of using GCN since one polygon should be corrected while its neighbors are taken into account for more accurate correction. (2) GCN inputs are provided by a layout encoder, which extracts a feature from each layout polygon. GCN outputs, features corresponding to corrected polygons, are processed by a mask decoder to yield the final mask pattern. (3) The encoder and decoder originate from respective autoencoders. High fidelity of decoder is a key for OPC quality. This is achieved by collective training of the two autoencoders with a single loss function while the encoder and decoder are connected. Experiments demonstrate that the proposed OPC achieves 47% smaller EPE than OPC using a simple MLP model.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2023-11
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, v.36, no.4, pp.629 - 635

ISSN
0894-6507
DOI
10.1109/TSM.2023.3306751
URI
http://hdl.handle.net/10203/315219
Appears in Collection
EE-Journal Papers(저널논문)
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