Enhancement of mask data verification with Smart-MRC and machine learning

Fei-Lin Liu; Shu-Yi Chou; Chi-Ian Ip; Hui-Min Leong; Min-Ru Wei; Po-Sheng Wang; Liang-Chieh Lin; Saghir Munir

doi:10.1117/12.3034282

26 August 2024 Enhancement of mask data verification with Smart-MRC and machine learning

Fei-Lin Liu, Shu-Yi Chou, Chi-Ian Ip, Hui-Min Leong, Min-Ru Wei, Po-Sheng Wang, Liang-Chieh Lin, Saghir Munir

Proceedings Volume 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology; 131770X (2024) https://doi.org/10.1117/12.3034282
Event: Photomask Japan 2024, 2024, Yokohama, Japan

Abstract

In the photomask manufacturing industry, photomask source design data needs to be converted into several different target formats, such as MEBES fracture, writer file, die-to-database inspection data etc. Due to the various conversion needs in the manufacturing flow, different EDA tools from different software vendors are employed during conversion. Two different EDA tools that are given the same input can result in slight differences in the output pattern and this will lead to causation of CD errors relative to the underlying pattern tolerances and/or specifications. During the photomask production process, it is very challenging to identify and classify these small differences in the output pattern caused by the conversion of data. In this study, we developed a novel solution to alert on pattern discrepancy by utilizing the classification generated by the application of machine leaning techniques and Smart-MRC tools. A Convolutional Neural Network (CNN) model is being introduced in this study and is trained by learning pre-classified data and classification result would be generated after inputting data to the CNN model. This new Mask Data Preparation (MDP) technique is expected to reduce the use of human resources in the classification process and will bring significant enhancement to our data validation steps to ensure pattern integrity across the entire photomask manufacturing tool chain. Furthermore, the risk of anomalies introduced by updating EDA software tools and their respective version is also mitigated.

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

Citation Download Citation

Fei-Lin Liu, Shu-Yi Chou, Chi-Ian Ip, Hui-Min Leong, Min-Ru Wei, Po-Sheng Wang, Liang-Chieh Lin, and Saghir Munir "Enhancement of mask data verification with Smart-MRC and machine learning", Proc. SPIE 13177, Photomask Japan 2024: XXX Symposium on Photomask and Next-Generation Lithography Mask Technology, 131770X (26 August 2024); https://doi.org/10.1117/12.3034282

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KEYWORDS

Data conversion

Inspection

Machine learning

Electronic design automation

Manufacturing

Data modeling

Photomasks

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