Machine Learning-Based Root Cause Analysis of Process Cycle Images
AssigneeIllumina, Inc.
InventorsNaghmeh REZAEI, Pedro Miguel FILIPE CRUZ
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Abstract
The technology disclosed relates to classification of process cycle images to predict success or failure of process cycles. The technology disclosed includes capturing and processing images of sections arranged on an image generating chip in genotyping process. Image description features of production cycle images are created and given as input to classifiers. A trained classifier separates successful production images from unsuccessful or failed production images. The failed production images are further classified by a trained root cause classifier into various categories of failure.
Claims20
A method of training a random forest classifier for genotyping process cycle images, including:
- accessing labelled training examples for images from process cycles belonging to a successful class and multiple failure classes;
- creating image description features for each labelled training example based on a linear combination of Eigen images;
- training the random forest classifier using the image description features of the labelled training examples;
- and
- storing parameters of the trained random forest classifier.
A non-transitory computer readable storage medium impressed with computer program instructions to train a random forest classifier for genotyping process cycle images, the instructions, when executed on a processor, implement a method comprising:
- accessing labelled training examples for images from process cycles belonging to a successful class and multiple failure classes;
- creating image description features for each labelled training example based on a linear combination of Eigen images;
- training the random forest classifier using the image description features of the labelled training examples;
- and
- storing parameters of the trained random forest classifier.
A non-transitory computer readable storage medium impressed with computer program instructions to classify genotyping process cycle images, the instructions, when executed on a processor, implement a method comprising:
- determining and applying a threshold, from intensities of pixels in a grayscale production cycle image, with which to classify image pixels into bright and dark classes and transform the grayscale production cycle image to a binary image;
- segmenting the grayscale image and the transformed binary image into eight or more areas that separate systematic expected noise in the image from signal;
- calculating average intensity values for the areas in the grayscale image and the transformed binary image;
- accessing a random forest classifier trained to classify the images from process cycles, based on the average intensity values for the areas in the grayscale and the binary images, as belonging to a successful class or to a failure class and comparing a confidence score generated by the trained random forest classifier to a threshold and classifying the image as successful or failed.
Dependencies are inferred from the claim text.