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Training a neural network for defect detection in low resolution images

  • US 10,599,951 B2
  • Filed: 03/25/2019
  • Issued: 03/24/2020
  • Est. Priority Date: 03/28/2018
  • Status: Active Grant
First Claim
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1. A system configured to train a neural network for defect detection in low resolution images, comprising:

  • an inspection tool comprising a high resolution imaging subsystem and a low resolution imaging subsystem, wherein the high and low resolution imaging subsystems comprise at least an energy source and a detector, wherein the energy source is configured to generate energy that is directed to a specimen, and wherein the detector is configured to detect energy from the specimen and to generate images responsive to the detected energy;

    one or more computer subsystems configured for acquiring the images of the specimen generated by the high and low resolution imaging subsystems; and

    one or more components executed by the one or more computer subsystems, wherein the one or more components comprise a high resolution neural network and a low resolution neural network; and

    wherein the one or more computer subsystems are further configured for;

    generating a training set of defect images, wherein at least one of the defect images is generated synthetically by the high resolution neural network using at least one of the images generated by the high resolution imaging subsystem;

    training the low resolution neural network using the training set of defect images as input; and

    detecting defects on another specimen by inputting the images generated for the other specimen by the low resolution imaging subsystem into the trained low resolution neural network.

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