Active learning-based defect location identification

Patent metadata

Field	Value
Publication number	US-12548141-B2
Application number	US-202118033786-A
Country	US
Kind code	B2
Filing date	Nov 2, 2021
Priority date	Nov 13, 2020
Publication date	Feb 10, 2026
Grant date	Feb 10, 2026

Abstract

A method and apparatus for identifying locations to be inspected on a substrate is disclosed. A defect location prediction model is trained using a training dataset associated with other substrates to generate a prediction of defect or non-defect and a confidence score associated with the prediction for each of the locations based on process-related data associated with the substrates. Those of the locations determined by the defect location prediction model as having confidences scores satisfying a confidence threshold are added to a set of locations to be inspected by an inspection system. After the set of locations are inspected, the inspection results data is obtained, and the defect location prediction model is incrementally trained by using the inspection results data and process-related data for the set of locations as training data.

First claim

The invention claimed is:

1 . A non-transitory computer-readable medium having instructions that, when executed by a computer system, are configured to cause the computer system to at least:
select a plurality of locations on the substrate to inspect based on a first sub-model of a defect location prediction model that is trained using an initial training dataset associated with other substrates to generate a prediction of defect or non-defect for each of the locations; using a second sub-model of the defect location prediction model that is trained using the initial training dataset, generate a confidence score for each of the locations based on process-related data associated with the substrate, wherein the confidence score is indicative of a confidence in the prediction for the corresponding location; add each of the locations for which the confidence score satisfies one of a plurality of confidence thresholds to a set of locations to be inspected by an inspection system; obtain inspection results data; and incrementally train the defect location prediction model by providing the inspection results data and process-related data for the set of locations as training data to the defect location prediction model.

2 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to incrementally train the second sub-model are configured to cause the training in an iterative manner in which each iteration includes training of the first sub-model using inspection results data and process-related data of a different substrate that has not been inspected in any prior iterations.

3 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to add each of the locations are configured to cause the computer system to add each of the locations to the set of locations when the confidence score of the prediction of defect for the corresponding location exceeds a first confidence threshold of the confidence thresholds.

4 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to add each of the locations are configured to cause the computer system to add each of the locations to the set of locations when the confidence score of the prediction of defect or non-defect for the corresponding location is below a second confidence threshold of the confidence thresholds.

5 . The computer-readable medium of claim 1 , wherein the instructions are further configured to cause the computer system to determine a prediction accuracy of the defect location prediction model based on a number of correct predictions and a total number of predictions.

6 . The computer-readable medium of claim 5 , wherein the incremental training of the defect location prediction model increases the prediction accuracy.

7 . The computer-readable medium of claim 5 , wherein the instructions are further configured to cause the computer system to adjust the confidence thresholds based on a change in the prediction accuracy.

8 . The computer-readable medium of claim 7 , wherein the instructions configured to cause the computer system to adjust the confidence thresholds are configured to cause the computer system to decrease a first confidence threshold of the confidence thresholds as the prediction accuracy improves, wherein the first confidence threshold is used to select those of the locations for which the prediction of a defect is associated with the confidence score exceeding the first confidence threshold.

9 . The computer-readable medium of claim 7 , wherein the instructions configured to cause the computer system to adjust the confidence thresholds are configured to cause the computer system to decrease a second confidence threshold of the confidence thresholds as the prediction accuracy improves, wherein the second confidence threshold is used to select those of the locations for which the prediction of defect or non-defect is associated with the confidence score below the second confidence threshold.

10 . The computer-readable medium of claim 7 , wherein the instructions configured to cause the computer system to adjust the confidence thresholds are configured to cause the computer system to increase a first confidence threshold of the confidence thresholds as the prediction accuracy degrades, wherein the first confidence threshold is used to select those of the locations for which the prediction of defect is associated with the confidence score exceeding the first confidence threshold.

11 . The computer-readable medium of claim 7 , wherein the instructions configured to cause the computer system to adjust the confidence thresholds are configured to cause the computer system to increase a second confidence threshold of the confidence thresholds as the prediction accuracy degrades, wherein the second confidence threshold is used to select those of the locations for which the prediction of defect or non-defect is associated with the confidence score below the second confidence threshold.

12 . The computer-readable medium of claim 1 , wherein the first sub-model is configured to generate a probability value for each of the predictions, the probability value indicative of a probability that the corresponding location is a defect location or a non-defect location.

13 . The computer-readable medium of claim 1 , wherein the instructions configured to cause the computer system to generate the confidence score are configured to cause the computer system to generate the confidence score for a specified location of the locations based on a comparison of process-related data associated with the specified location and process-related data in the initial training dataset or the training data used to train the defect location prediction model.

14 . The computer-readable medium of claim 1 , wherein the defect location prediction model includes a plurality of first sub-models, and wherein the instructions configured to cause the computer system to generate the confidence score are configured to cause the computer system to:
obtain, from each of the first sub-models, a probability value associated with the prediction for a specified location of the locations, and generate the confidence score for the specified location as a function of the probability values obtained from the first sub-models.

15 . An apparatus for identifying locations to inspect on a first substrate using a machine learning model and for training the machine learning model to identify locations to inspect on a second substrate based on inspection results of the locations on the first substrate, the apparatus comprising:
a memory storing a set of instructions; and at least one processor configured to execute the set of instructions to cause the apparatus to at least: input process-related data associated with a substrate to a defect location prediction model; generate, using the defect location prediction model, a prediction of defect or non-defect for each of a plurality of locations on the substrate, wherein each prediction is associated with a confidence score that is indicative of a confidence in the prediction for the corresponding location; add each of the locations for which the confidence score satisfies a confidence threshold to a set of locations to be inspected by an inspection system; obtain inspection results data for the set of locations from the inspection system; and input the inspection results data and process-related data for the set of locations to the defect location prediction model for training the defect location p

Assignees

• Asml Netherlands Bv

Inventors

• Lin Chenxi
• Zou Yi
• Hasan Tanbir
• Xu Huina
• Kou Ren-Jay
• Moin Nabeel Noor
• Nafisi Kourosh

Classifications

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/20081

  Training; Learning · CPC title

• G03F7/706833

  Sampling plan selection or optimisation, e.g. select or optimise the number, order or locations of measurements taken per die, workpiece, lot or batch · CPC title

• G03F7/705

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

• G03F7/706841 Primary

  Machine learning · CPC title