Invention Grant
- Patent Title: Measuring defectivity by equipping model-less scatterometry with cognitive machine learning
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Application No.: US15899197Application Date: 2018-02-19
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Publication No.: US10692203B2Publication Date: 2020-06-23
- Inventor: Dexin Kong , Robin Hsin Kuo Chao , Huai Huang
- Applicant: International Business Machines Corporation
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agent Vazken Alexanian; Michael J. Chang, LLC
- Main IPC: G06T7/00
- IPC: G06T7/00 ; G06N20/00

Abstract:
Techniques for measuring defectivity using model-less scatterometry with cognitive machine learning are provided. In one aspect, a method for defectivity detection includes: capturing SEM images of defects from a plurality of training wafers; classifying type and density of the defects from the SEM images; making training scatterometry scans of a same location on the training wafers as the SEM images; training a machine learning model to correlate the training scatterometry scans with the type and density of the defects from the same location in the SEM images; making scatterometry scans of production wafers; and detecting defectivity in the production wafers by measuring the type and density of the defects in the production wafers using the machine learning model, as trained, and the scatterometry scans of the production wafers. A system for defectivity detection is also provided.
Public/Granted literature
- US20190259145A1 Measuring Defectivity by Equipping Model-Less Scatterometry with Cognitive Machine Learning Public/Granted day:2019-08-22
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