Poster + Paper
30 April 2023 Predicting the critical features of the chemically-amplified resist profile based on machine learning
Author Affiliations +
Conference Poster
Abstract
The improvement of accuracy and efficiency in simulating the profile of the chemically amplified resist (CAR) is always a key point in lithography. With the development of machine learning, many models have been successfully applied in optical proximity correction (OPC), hotspot detection, and other lithographic fields. In this work, we developed a neural network for predicting the critical features’ sizes of the CAR profile. By using a pre-calibrated physical resist model, the effectiveness of this model is demonstrated from numerical simulation. The results indicate that for the critical dimensions (CDs) of the CAR profile, this model shows great speed and accuracy. After applying the tuned neural network on the test sets, it shows 92.98% of the test sets have a mean square error (MSE) less than 1%.
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Pengjie Kong, Lisong Dong, Xu Ma, and Yayi Wei "Predicting the critical features of the chemically-amplified resist profile based on machine learning", Proc. SPIE 12498, Advances in Patterning Materials and Processes XL, 124981U (30 April 2023); https://doi.org/10.1117/12.2658664
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KEYWORDS
Photoresist materials

Neural networks

Machine learning

Artificial neural networks

Cadmium sulfide

Chemically amplified resists

Photoresist developing

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