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US Patent 11921433 Optical metrology in machine learning to characterize features

Patent 11921433 was granted and assigned to Lam Research on March, 2024 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Lam Research
Lam Research
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Current Assignee
Lam Research
Lam Research
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119214330
Patent Inventor Names
Osman Sorkhabi0
Ye Feng0
Yan Zhang0
Date of Patent
March 5, 2024
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Patent Application Number
157337280
Date Filed
April 10, 2019
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Patent Citations
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US Patent 7600212 Method of compensating photomask data for the effects of etch and lithography processes
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US Patent 7739651 Method and apparatus to determine if a pattern is robustly manufacturable
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US Patent 7812966 Method of determining the depth profile of a surface structure and system for determining the depth profile of a surface structure
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US Patent 7849423 Method of verifying photomask data based on models of etch and lithography processes
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US Patent 7921383 Photolithographic process simulation including efficient result computation for multiple process variation values
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US Patent 6869739 Integrated lithographic print and detection model for optical CD
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US Patent 7139632 Enhanced process and profile simulator algorithms
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US Patent 7402257 Plasma state monitoring to control etching processes and across-wafer uniformity, and system for performing same
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Patent Primary Examiner
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An H. Do
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CPC Code
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G03F 7/70616
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G03F 7/70625
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G03F 7/705
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G06N 20/00
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G06N 3/08
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G01N 23/201
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G01N 21/211
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G01N 21/55
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Patent abstract

A metrology system may include an optical metrology tool configured to produce an optical metrology output for one or more features on a processed substrate, and a metrology machine learning model that has been trained using a training set of (i) profiles, critical dimensions, and/or contours for a plurality of features, and (ii) optical metrology outputs for the plurality of features. The metrology machine learning model may be configured to: receive the optical metrology output from the optical metrology tool; and output the profile, critical dimension, and/or contour of the one or more features on the processed substrate.

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