公开(公告)号：US11747740B2

公开(公告)日：2023-09-05

申请号：US17790765

申请日：2021-01-06

Applicant: NOVA LTD

Inventor： Ran Yacoby ,  Boaz Sturlesi

IPC: G01N21/47 ,  G03F7/20 ,  G06N3/08 ,  G01B11/02 ,  G01N21/956 ,  G06T1/40 ,  G03F7/00

CPC classification number: G03F7/70625 ,  G03F7/70508 ,  G06N3/08

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.

公开(公告)号：US11815819B2

公开(公告)日：2023-11-14

申请号：US17995706

申请日：2021-04-06

Applicant: NOVA LTD.

Inventor： Barak Bringoltz ,  Ran Yacoby ,  Noam Tal ,  Shay Yogev ,  Boaz Sturlesi ,  Oded Cohen

IPC: G05B13/02 ,  G03F7/00 ,  G03F7/20

CPC classification number: G03F7/70508 ,  G03F7/705 ,  G03F7/70525 ,  G05B13/0265 ,  G05B2219/2602 ,  G05B2219/45031

Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.