Illumina, Inc.
DEEP LEARNING-BASED ROOT CAUSE ANALYSIS OF PROCESS CYCLE IMAGES

Abstract:

The technology disclosed relates to training a convolutional neural network (CNN) to identify and classify images of sections of an image generating chip resulting in process cycle failures. The technology disclosed includes creating a training data set of images of dimensions M.times.N using labeled images of sections of image generating chip of dimensions J.times.K. The technology disclosed can fill the M.times.N frames using horizontal and vertical reflections along edges of J.times.K labeled images positioned in M.times.N frames. A pretrained CNN is further trained using the training data set. Trained CNN can classify a section image as normal or depicting failure. The technology disclosed can train a root cause CNN to classify process cycle images of sections causing process cycle failure. The trained CNN can classify a section image by root cause of process failure among a plurality of failure categories.