Prediction of etch bias using random forest model

Wenrui Wang; Hua Shao; Rui Chen; Yayi Wei

doi:10.1117/12.3010574

10 April 2024 Prediction of etch bias using random forest model

Wenrui Wang, Hua Shao, Rui Chen, Yayi Wei

Proceedings Volume 12954, DTCO and Computational Patterning III; 129541F (2024) https://doi.org/10.1117/12.3010574
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

Etching process is an indispensable patterning step in semiconductor device manufacturing. The etch bias compensation is critical in optical proximity correction (OPC) to ensure lithography fidelity and device performance. Therefore, accurate prediction of etch bias has become more crucial as moving to advanced technology node etching process. This study aims to develop an etch bias prediction model based on ensemble learning, specifically utilizing the Random Forest algorithm. A substantial simulation results comprising linewidth, pitch, and corresponding etch bias data for one-dimensional layouts was collected. Subsequently, we employed the Random Forest algorithm, a powerful ensemble learning method, to construct the etch bias prediction model. Random Forest effectively captures the intricate relationships between linewidth, pitch, and etch bias by combining multiple decision trees. Finally, we utilized transfer learning techniques to fine-tune a pre-trained random forest model using real experimental data, resulting in the final model. Compared to traditional machine learning methods, such as the BP neural network, this approach features with faster training speed and better robustness, the Random Forest model exhibits stronger transferability across different technology nodes and different process conditions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenrui Wang, Hua Shao, Rui Chen, and Yayi Wei "Prediction of etch bias using random forest model", Proc. SPIE 12954, DTCO and Computational Patterning III, 129541F (10 April 2024); https://doi.org/10.1117/12.3010574

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KEYWORDS

Etching

Data modeling

Random forests

Education and training

Machine learning

Calibration

Decision trees

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