Invention Grant
- Patent Title: Self-supervised representation learning for interpretation of OCD data
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Application No.: US17790765Application Date: 2021-01-06
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Publication No.: US11747740B2Publication Date: 2023-09-05
- Inventor: Ran Yacoby , Boaz Sturlesi
- Applicant: NOVA LTD
- Applicant Address: IL Rehovot
- Assignee: NOVA LTD
- Current Assignee: NOVA LTD
- Current Assignee Address: IL Rehovot
- Agency: ALPHAPATENT ASSOCIATES, LTD
- Agent Daniel J. Swirsky
- International Application: PCT/IL2021/050018 2021.01.06
- International Announcement: WO2021/140508A 2021.07.15
- Date entered country: 2022-07-05
- Main IPC: G01N21/47
- IPC: G01N21/47 ; G03F7/20 ; G06N3/08 ; G01B11/02 ; G01N21/956 ; G06T1/40 ; G03F7/00

Abstract:
A system and methods for OCD metrology are provided including receiving multiple first sets of scatterometric data, dividing each set into k sub-vectors, and training, in a self-supervised manner, k2 auto-encoder neural networks that map each of the k sub-vectors to each other. Subsequently multiple respective sets of reference parameters and multiple corresponding second sets of scatterometric data are received and a transfer neural network (NN) is trained. Initial layers include a parallel arrangement of the k2 encoder neural networks. Target output of the transfer NN training is set to the multiple sets of reference parameters and feature input is set to the multiple corresponding second sets of scatterometric data, such that the transfer NN is trained to estimate new wafer pattern parameters from subsequently measured sets of scatterometric data.
Public/Granted literature
- US20230014976A1 SELF-SUPERVISED REPRESENTATION LEARNING FOR INTERPRETATION OF OCD DATA Public/Granted day:2023-01-19
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