Machine Learning-Enhanced Model-Based Optical Proximity Correction by Using Convolutional Neural Network-Based Variable Threshold Method

Machine Learning-Enhanced Model-Based Optical Proximity Correction by Using Convolutional Neural Network-Based Variable Threshold Method

As the lithography process continues to become more rigorous in advanced technology nodes, the model-based optical proximity correction (MBOPC), as a core component within computational lithography, necessitates the development of highly precise techniques. In this paper, we propose an approach to e...

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Bibliographic Details
Main Authors: Jinhao Zhu, Zhiwei Ren, Ying Li, Xianhe Liu, Qiang Wu, Yanli Li, Qi Wang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Computational lithography
optical proximity correction (OPC)
convolutional neural network
variable threshold
Online Access:https://ieeexplore.ieee.org/document/10802911/
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https://ieeexplore.ieee.org/document/10802911/

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