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Fast Optical Proximity Correction Using Graph Convolutional Network With Autoencoders.
- Source :
-
IEEE Transactions on Semiconductor Manufacturing . Nov2023, Vol. 36 Issue 4, p629-635. 7p. - Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08946507
- Volume :
- 36
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Semiconductor Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 173370009
- Full Text :
- https://doi.org/10.1109/TSM.2023.3306751