• Patent Title: SELF-SUPERVISED REPRESENTATION LEARNING FOR INTERPRETATION OF OCD DATA
  • Application No.: US18241923
    Application Date: 2023-09-04
  • Publication No.: US20240069445A1
    Publication Date: 2024-02-29
  • Inventor: RAN YACOBYBOAZ STURLESI
  • Applicant: NOVA LTD
  • Applicant Address: IL REHOVOT
  • Assignee: NOVA LTD
  • Current Assignee: NOVA LTD
  • Current Assignee Address: IL REHOVOT
  • Main IPC: G03F7/00
  • IPC: G03F7/00 G06N3/08
SELF-SUPERVISED REPRESENTATION LEARNING FOR INTERPRETATION OF OCD DATA
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.
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