Identification of hot spots or defects by machine learning

Patent metadata

Field	Value
Publication number	US-11443083-B2
Application number	US-201716300380-A
Country	US
Kind code	B2
Filing date	Apr 20, 2017
Priority date	May 12, 2016
Publication date	Sep 13, 2022
Grant date	Sep 13, 2022

Abstract

Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

First claim

What is claimed is:

1. A method comprising:
obtaining a plurality of sets of one or more characteristics of performance of a hot spot under a plurality of process conditions in a device manufacturing process, respectively;
determining, for each of the process conditions, based on the set of one or more characteristics under that process condition, whether the hot spot is defective;
obtaining a characteristic of each of the process conditions; and
training, by a hardware computer system, a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and a label comprising whether the hot spot is defective under that process condition.

2. The method of claim 1 , wherein the characteristic of each of the process conditions comprises focus, dose, a reticle map, moving standard deviation (MSD), or a chemical-mechanical planarization (CMP) heat map.

3. The method of claim 1 , wherein the plurality of sets of one or more characteristics of performance comprise a characteristic of an image of the hot spot produced by the device manufacturing process under the respective process condition.

4. The method of claim 1 , wherein determining whether the hot spot is defective comprises comparing a characteristic of performance to a specification for the hot spot.

5. The method of claim 1 , wherein obtaining the plurality of sets of one or more characteristics of performance comprises performing a simulation, performing metrology, or performing comparison of a characteristic of performance to empirical data.

6. A method comprising:
obtaining a plurality of sets of one or more characteristics of performance of a plurality of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process;
determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective;
obtaining a characteristic of each of the process conditions;
obtaining a characteristic of each of the hot spots; and
training, by a hardware computer system, a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and the respective characteristic of one of the hot spots, the feature vector further comprising a label comprising whether that hot spot is defective under that process condition.

7. The method of claim 6 , wherein the plurality of sets of one or more characteristics of performance comprise a characteristic of an image of the respective hot spot produced by the device manufacturing process under the respective process conditions.

8. The method of claim 6 , wherein obtaining the plurality of sets of one or more characteristics of performance comprises performing a simulation, performing metrology, or performing comparison of a characteristic of performance to empirical data.

9. The method of claim 6 , wherein the characteristic of each of the process conditions comprises focus, dose, a reticle map, moving standard deviation (MSD), or a chemical-mechanical planarization (CMP) heat map.

10. The method of claim 6 , wherein at least one of the characteristics of the hot spots comprises a characteristic of geometric shape of a hot spot, a density distribution of a pixelated image of a hot spot, a result of functional decomposition of a hot spot, fragmentation of a hot spot, diffraction order distribution of a hot spot, a Bossung curve for a hot spot, or a geometric characteristic of a hot spot.

11. A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain a plurality of sets of one or more characteristics of performance of a hot spot under a plurality of process conditions in a device manufacturing process, respectively;
determine, for each of the process conditions, based on the set of one or more characteristics under that process condition, whether the hot spot is defective;
obtain a characteristic of each of the process conditions; and
train a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and a label comprising whether the hot spot is defective under that process condition.

12. The computer program product of claim 11 , wherein the characteristic of each of the process conditions comprises focus, dose, a reticle map, moving standard deviation (MSD), or a chemical-mechanical planarization (CMP) heat map.

13. The computer program product of claim 11 , wherein the plurality of sets of one or more characteristics of performance comprise a characteristic of an image of the hot spot produced by the device manufacturing process under the respective process condition.

14. The computer program product of claim 11 , wherein the instructions configured to cause the computer system to determine whether the hot spot is defective are further configured to cause the computer system to compare a characteristic of performance to a specification for the hot spot.

15. The computer program product of claim 11 , wherein the instructions configured to cause the computer system to obtain the plurality of sets of one or more characteristics of performance are further configured to cause the computer system to perform a simulation, cause performance of metrology, or perform comparison of a characteristic of performance to empirical data.

16. A computer program product comprising a non-transitory computer readable medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain a plurality of sets of one or more characteristics of performance of a plurality of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process;
determine, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective;
obtain a characteristic of each of the process conditions;
obtain a characteristic of each of the hot spots; and
train a machine learning model using a training set comprising a plurality of samples, wherein each of the samples has a feature vector comprising the respective characteristic of one of the process conditions and the respective characteristic of one of the hot spots, the feature vector further comprising a label comprising whether that hot spot is defective under that process condition.

17. The computer program product of claim 16 , wherein the plurality of sets of one or more characteristics of performance comprise a characteristic of an image of the respective hot spot produced by the device manufacturing process under the respective process conditions.

18. The computer program product of claim 16 , wherein the instructions configured to cause the computer system to obtain the plurality of sets of one or more characteristics of performance are further configured to cause the computer system to perform a simulation, cause performance of metrology, or perform comparison of a characteristic of performance to empirical data.

Assignees

• Asml Netherlands Bv

Inventors

• Su Jing
• Zou Yi
• Lin Chenxi
• Hunsche Stefan
• Jochemsen Marinus
• Lu Yen-Wen
• Cheong Lin Lee

Classifications

• G03F7/705 Primary

  Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions · CPC title

• G06V10/772

  Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries · CPC title

• G06F30/20 Primary

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

• G06F18/28

  Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries · CPC title

• G03F7/70525

  Controlling normal operating mode, e.g. matching different apparatus, remote control or prediction of failure · CPC title