US 11,379,967 B2Granted

Methods and systems for inspection of semiconductor structures with automatically generated defect features

AssigneeKLA Corporation

InventorsJacob George, Saravanan Paramasivam, Martin Plihal, Niveditha Lakshmi Narasimhan, Sairam Ravu, Prasanti Uppaluri

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Abstract

Methods and systems for improved detection and classification of defects of interest (DOI) is realized based on values of one or more automatically generated attributes derived from images of a candidate defect. Automatically generated attributes are determined by iteratively training, reducing, and retraining a deep learning model. The deep learning model relates optical images of candidate defects to a known classification of those defects. After model reduction, attributes of the reduced model are identified which strongly relate the optical images of candidate defects to the known classification of the defects. The reduced model is subsequently employed to generate values of the identified attributes associated with images of candidate defects having unknown classification. In another aspect, a statistical classifier is employed to classify defects based on automatically generated attributes and attributes identified manually.

Claims20

Dependent claims

A method comprising:

generating one or more training images including one or more candidate defects, each of the one or more candidate defects having a known defect classification;
training a deep learning based model having the one or more training images of the one or more candidate defects as input to the deep learning model and the known classification of each of the one or more candidate defects as output of the deep learning model, wherein the deep learning based model is a neural network model including at least one convolutional layer including a plurality of image filters and at least one connected layer including a plurality of nodes;
determining a reduced deep learning model by reducing a number of layers of the neural network model, a number of image filters of the neural network model, a number of nodes of the neural network model, or any combination thereof;
retraining the reduced deep learning based model having the one or more images of the one or more candidate defects as input to the reduced deep learning model and the known classification of each of the one or more candidate defects as output of the reduced deep learning model;
determining a value of each of one or more attributes of the reduced deep learning based model associated with each of the one or more training images;
and
training a statistical classifier including the value of each of the one or more attributes of the reduced deep learning based model associated with each of the one or more training images as input to the statistical classifier and the known classification associated with each of the one or more training images as output of the statistical classifier.

4 more elements

A system comprising:

an illumination source providing a first amount of illumination light to a semiconductor wafer at locations of one or more candidate defects on a semiconductor wafer, each of the one or more candidate defects having a known defect classification;
a detector detecting an amount of light from the semiconductor wafer in response to the first amount of illumination light and generating a plurality of training images including the one or more candidate defects;
and
a computing system configured to:
train a deep learning based model having the one or more training images of the one or more candidate defects as input to the deep learning model and the known classification of each of the one or more candidate defects as output of the deep learning model, wherein the deep learning based model is a neural network model including at least one convolutional layer including a plurality of image filters and at least one connected layer including a plurality of nodes;
determine a reduced deep learning model by reducing a number of layers of the neural network model, a number of image filters of the neural network model, a number of nodes of the neural network model, or any combination thereof;
retrain the reduced deep learning based model having the one or more images of the one or more candidate defects as input to the reduced deep learning model and the known classification of each of the one or more candidate defects as output of the reduced deep learning model;
determine a value of each of one or more attributes of the reduced deep learning based model associated with each of the one or more training images;
and
train a statistical classifier including the value of each of the one or more attributes of the reduced deep learning based model associated with each of the one or more training images as input to the statistical classifier and the known classification associated with each of the one or more training images as output of the statistical classifier.

7 more elements

A system comprising:

an illumination source providing a first amount of illumination light to a semiconductor wafer at locations of one or more candidate defects on a semiconductor wafer, each of the one or more candidate defects having a known defect classification;
a detector detecting an amount of light from the semiconductor wafer in response to the first amount of illumination light and generating a plurality of training images including the one or more candidate defects;
and
a computing system comprising:
one or more processors;
and
a non-transitory, computer-readable medium storing instructions that when executed by the one or more processors, cause the computing system to:
train a deep learning based model having the one or more training images of the one or more candidate defects as input to the deep learning model and the known classification of each of the one or more candidate defects as output of the deep learning model, wherein the deep learning based model is a neural network model including at least one convolutional layer including a plurality of image filters and at least one connected layer including a plurality of nodes;
determine a reduced deep learning model by reducing a number of layers of the neural network model, a number of image filters of the neural network model, a number of nodes of the neural network model, or any combination thereof;
retrain the reduced deep learning based model having the one or more images of the one or more candidate defects as input to the reduced deep learning model and the known classification of each of the one or more candidate defects as output of the reduced deep learning model;
determine a value of each of one or more attributes of the reduced deep learning based model associated with each of the one or more training images;
and
train a statistical classifier including the value of each of the one or more attributes of the reduced deep learning based model associated with each of the one or more training images as input to the statistical classifier and the known classification associated with each of the one or more training images as output of the statistical classifier.

10 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/744,385Filed 16 Jan 2020 · us provisional application
Priority: 6 Sep 2019
Published: US 2020/0234428 A123 Jul 2020
Granted: 5 Jul 2022
Term ends: Not estimatedThe source records don’t support an estimate.

Classification22 codes

G06T 7/0004
Inspection of images, e.g. flaw detection: industrial image inspection
G06F 18/2148
Generating training patterns; Bootstrap methods, e.g. bagging or boosting: characterised by the process organisation or structure, e.g. boosting…
G06F 18/24
Analysing: classification techniques
G06F 18/2413
Relating to the classification model, e.g. parametric or non-parametric approaches: based on distances to training or reference patterns