Invention Publication
- Patent Title: DEEP LEARNING BASED ADAPTIVE ALIGNMENT PRECISION METROLOGY FOR DIGITAL OVERLAY
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Application No.: US18034903Application Date: 2021-11-30
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Publication No.: US20230408932A1Publication Date: 2023-12-21
- Inventor: Tamer COSKUN , Yen-Shuo LIN , Aidyn KEMELDINOV
- Applicant: Applied Materials, Inc.
- Applicant Address: US CA Santa Clara
- Assignee: Applied Materials, Inc.
- Current Assignee: Applied Materials, Inc.
- Current Assignee Address: US CA Santa Clara
- International Application: PCT/US2021/061088 2021.11.30
- Date entered country: 2023-05-02
- Main IPC: G03F7/00
- IPC: G03F7/00 ; G03F9/00 ; G06V10/75 ; G06V10/764 ; G06T7/73 ; G06T7/33 ; G06T7/00

Abstract:
Embodiments described herein relate to a system, methods, and non-transitory computer-readable mediums that accurately align subsequent patterned layers in a photoresist utilizing a deep learning model and utilizing device patterns to replace alignment marks in lithography processes. The deep learning model is trained to recognize unique device patterns called alignment patterns in the FOV of the camera. Cameras in the lithography system capture images of the alignment patterns. The deep learning model finds the alignment patterns in the field of view of the cameras. An ideal image generated from a design file is matched with the camera with respect to the center of the field of view of the camera. A shift model and a rotation model are output from the deep learning model to create an alignment model. The alignment model is applied to the currently printing layer.
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