DEEP LEARNING-BASED ROOT CAUSE ANALYSIS OF PROCESS CYCLE IMAGES

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×N using labeled imag...

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Bibliographische Detailangaben
Hauptverfasser:	GIETZEN, Kimberly Jean, LIU, Jingtao, TAO, Yifeng
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING IMAGE DATA PROCESSING OR GENERATION, IN GENERAL INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING PHYSICS TESTING
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Beschreibung
Zusammenfassung:	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×N using labeled images of sections of image generating chip of dimensions J×K. The technology disclosed can fill the M×N frames using horizontal and vertical reflections along edges of J×K labeled images positioned in M×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.