公开(公告)号：US11580375B2

公开(公告)日：2023-02-14

申请号：US15394790

申请日：2016-12-29

Applicant: KLA-Tencor Corporation

Inventor： Kris Bhaskar ,  Laurent Karsenti ,  Scott Young ,  Mohan Mahadevan ,  Jing Zhang ,  Brian Duffy ,  Li He ,  Huajun Ying ,  Hung Nien ,  Sankar Venkataraman

IPC: G06N3/08 ,  G01Q30/02 ,  G06N3/067 ,  G06N20/00 ,  G06N3/04 ,  G06F30/20

Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.

公开(公告)号：US20170193400A1

公开(公告)日：2017-07-06

申请号：US15394790

申请日：2016-12-29

Applicant: KLA-Tencor Corporation

Inventor： Kris Bhaskar ,  Laurent Karsenti ,  Scott Young ,  Mohan Mahadevan ,  Jing Zhang ,  Brian Duffy ,  Li He ,  Huajun Ying ,  Hung Nien ,  Sankar Venkataraman

IPC: G06N99/00 ,  G06F17/50

Abstract: Methods and systems for accelerated training of a machine learning based model for semiconductor applications are provided. One method for training a machine learning based model includes acquiring information for non-nominal instances of specimen(s) on which a process is performed. The machine learning based model is configured for performing simulation(s) for the specimens. The machine learning based model is trained with only information for nominal instances of additional specimen(s). The method also includes re-training the machine learning based model with the information for the non-nominal instances of the specimen(s) thereby performing transfer learning of the information for the non-nominal instances of the specimen(s) to the machine learning based model.