@inproceedings{847d9cc3c41f41038aa2f7098ce4aa97,
title = "Predicting the critical features of the chemically-amplified resist profile based on machine learning",
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%.",
keywords = "chemically amplified resist, critical feature, machine learning, neural network., predict",
author = "Pengjie Kong and Lisong Dong and Xu Ma and Yayi Wei",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Advances in Patterning Materials and Processes XL 2023 ; Conference date: 27-02-2023 Through 01-03-2023",
year = "2023",
doi = "10.1117/12.2658664",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Douglas Guerrero and Amblard, {Gilles R.}",
booktitle = "Advances in Patterning Materials and Processes XL",
address = "United States",
}
