A deep learning mask analysis toolset using mask SEM digital twins

Ajay K. Baranwal; Suhas Pillai; Thang Nguyen; Jun Yashima; Jim Dewitt; Noriaki Nakayamada; Mikael Wahlsten; Aki Fujimura

doi:10.1117/12.2576431

16 October 2020 A deep learning mask analysis toolset using mask SEM digital twins

Ajay K. Baranwal, Suhas Pillai, Thang Nguyen, Jun Yashima, Jim Dewitt, Noriaki Nakayamada, Mikael Wahlsten, Aki Fujimura

Author Affiliations +

Proceedings Volume 11518, Photomask Technology 2020; 1151814 (2020) https://doi.org/10.1117/12.2576431
Event: SPIE Photomask Technology + EUV Lithography, 2020, Online Only

Abstract

Sub-nanometer accuracy attainable with electron micrograph SEM images is the only way to “see” well enough for the mask analysis needed in EUV mask production. Because SEM images are pixel dose maps, deep learning (DL) offers an attractive alternative to the tedious and error-prone mask analysis performed by the operators and expert field application engineers in today’s mask shops. However, production demands preclude collecting a large enough variety and number of real SEM images to effectively train deep learning models. We have found that digital twins that can mimic the SEM images derived from CAD data provide an exceptional way to synthesize ample data to train effective DL models. Previous studies [1, 2, 3, 4] have shown how deep learning can be used to create digital twins. However, it was unclear if SEM images generated with digital twins would have sufficient quality to train a deep learning network to classify real SEM images. This paper shows how we built three DL tools for SEM-based mask analysis. The first tool automatically filters good quality SEM images, particularly for test chips, using a DL-based binary classifier. A second tool uses another DL model to align CAD and SEM images for applications where it is important that features on both the images are properly aligned. A third tool uses a DL multi-class classifier to categorize various types of VSB mask writer defects. In developing the three tools, we trained state-of-the-art deep neural networks on SEM images generated using digital twins to achieve accurate results on real SEM images. Furthermore, we validated the results of trained deep learning models through model visualization and accuracy-metric evaluation.

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Ajay K. Baranwal, Suhas Pillai, Thang Nguyen, Jun Yashima, Jim Dewitt, Noriaki Nakayamada, Mikael Wahlsten, and Aki Fujimura "A deep learning mask analysis toolset using mask SEM digital twins", Proc. SPIE 11518, Photomask Technology 2020, 1151814 (16 October 2020); https://doi.org/10.1117/12.2576431

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