Accelerated training of a machine learning based model for semiconductor applications

Patent metadata

Field	Value
Publication number	US-2017193400-A1
Application number	US-201615394790-A
Country	US
Kind code	A1
Filing date	Dec 29, 2016
Priority date	Dec 31, 2015
Publication date	Jul 6, 2017
Grant date	—

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.

First claim

What is claimed is:

1 . A system configured to train a machine learning based model, comprising:
one or more computer subsystems; and one or more components executed by the one or more computer subsystems, wherein the one or more components comprise:
a machine learning based model configured for performing one or more simulations for specimens, wherein the machine learning based model is trained with only information for nominal instances of one or more of the specimens;
wherein the one or more computer subsystems are configured for:
acquiring information for non-nominal instances of one or more of the specimens on which a process is performed; and
re-training the machine learning based model with the information for the non-nominal instances of the one or more of the specimens thereby performing transfer learning of the information for the non-nominal instances of the one or more of the specimens to the machine learning based model.

2 . The system of claim 1 , wherein performing the one or more simulations comprises generating one or more simulated images for one of the specimens, and wherein the one or more simulated images illustrate how the one of the specimens appears in one or more actual images of the one of the specimens generated by an imaging system.

3 . The system of claim 2 , wherein the imaging system is an optical based imaging system.

4 . The system of claim 2 , wherein the imaging system is an electron beam based imaging system.

5 . The system of claim 1 , wherein performing the one or more simulations comprises generating one or more simulated measurements for one of the specimens, and wherein the one or more simulated measurements represent output generated for the one of the specimens by a metrology system.

6 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens.

7 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, and wherein the one or more specimens comprise one or more actual specimens on which the process is performed with two or more different values of one or more parameters of the process.

8 . The system of claim 7 , wherein the process is performed with the two or more different values of the one or more parameters of the process in a process window qualification method.

9 . The system of claim 7 , wherein the process is performed with the two or more different values of the one or more parameters of the process in a process window qualification method designed for overlay margin determination.

10 . The system of claim 7 , wherein the process is performed with the two or more different values of the one or more parameters of the process in a focus exposure matrix method.

11 . The system of claim 1 , wherein the acquired information is generated from synthetic design data for the one or more specimens produced by an electronic design automation tool.

12 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, and wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design.

13 . The system of claim 12 , wherein the one or more components further comprise an inception module configured for altering the design to create the synthetic defects in the design.

14 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, and wherein the information for the non-nominal instances comprises output generated by an imaging or metrology system for the one or more specimens on which the synthetic defects are printed.

15 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, wherein the information for the non-nominal instances comprises output of another model, and wherein the output of the other model represents the one or more specimens on which the synthetic defects are printed.

16 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, wherein the information for the non-nominal instances comprises output of another model, and wherein the output of the other model illustrates how the one or more specimens on which the synthetic defects are printed appear in one or more actual images of the specimen generated by an imaging system.

17 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, wherein the information for the non-nominal instances comprises output of another model, and wherein the output of the other model represents output generated by a metrology system for the one or more specimens on which the synthetic defects are printed.

18 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, wherein the information for the non-nominal instances comprises output of another model, wherein the output of the other model represents output generated by another system for the one or more specimens on which the synthetic defects are printed, and wherein the other model is a deep generative model.

19 . The system of claim 1 , wherein the non-nominal instances comprise instances of defects on the one or more specimens, wherein the defects comprise one or more synthetic defects generated by altering a design for the one or more specimens to create the synthetic defects in the design, and wherein the information for the non-nominal instances comprises the altered design.

20 . The system of claim 1 , wherein the one or more components further comprise a deep generative model configured to create the information for the nominal instances of the one or more specimens.

21 . The system of claim 1 , wherein the nominal instances of the one or more specimens comprise natural scene images.

22 . The system of claim 1 , wherein the nominal instances of the one or more specimens comprise more than one type of data.

23 . The system of claim 1 , wherein the machine learning based model is a discriminative model.

24 . The system of claim 1 , wherein the machine learning based model is a neural network.

25 . The system of claim 1 , wherein the machine learning based model is a convolution and deconvolution neural network.

26 . The system of claim 1 , wherein the one or more components further comprise one or more additional componen

Assignees

• Kla Tencor Corp

Inventors

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

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06N20/00 Primary

  Machine learning · CPC title