Machine learning in electron beam lithography to boost photoresist formulation design for high-resolution patterning

The reduction of the critical dimension (CD) usually improves the resolution of patterns and performance of chips. In chip manufacturing, electron beam lithography (EBL) is a promising technology for preparing sub-10 nm patterns, and its imaging resolution is primarily determined by the photoresist...

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Bibliographic Details
Published in:Nanoscale 2024-02, Vol.16 (8), p.4212-4218
Main Authors: Zhao, Rongbo, Wang, Xiaolin, Xu, Hong, Wei, Yayi, He, Xiangming
Format: Article
Language:English
Subjects:
Artificial intelligence
Electron beam lithography
High resolution
Image resolution
Machine learning
Metal oxides
Optimization
Optimization techniques
Photoresists
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Summary:The reduction of the critical dimension (CD) usually improves the resolution of patterns and performance of chips. In chip manufacturing, electron beam lithography (EBL) is a promising technology for preparing sub-10 nm patterns, and its imaging resolution is primarily determined by the photoresist formulation. However, the smaller CDs are mainly achieved by optimizing process conditions, and little attention has been paid to the photoresist formulation optimization. Screening suitable photoresist formulations remains a significant challenge due to the considerable time and high cost. Herein, we report a formulation optimization technique of a metal oxide nanoparticle photoresist that combines EBL experiments with a machine learning long short-term memory (LSTM) network. Using the LSTM network, a CD photoresist evaluation model is established. Leveraging the CD model, a photoresist formulation optimizer is developed with a line width of 26 nm. The verification results demonstrate that the CDs predicted by the LSTM network are basically consistent with the EBL experimental results, and the photoresist formulations that meet the CD requirements can be screened. This work opens up a novel perspective to boost photoresist formulation design for high-resolution patterning with artificial intelligence and provides guidance for EBL experiments. A high-precision photoresist imaging model and formulation optimizer for electron beam lithography are developed. The optimized photoresist formulation meets the preset imaging performance requirement, boosting photoresist material design.
ISSN:2040-3364
2040-3372
DOI:10.1039/d3nr04819e