Apparatuses and methods for determining wafer defects

An inspection system for determining wafer defects in semiconductor fabrication may include an image capturing device to capture a wafer image and a classification convolutional neural network (CNN) to determine a classification from a plurality of classes for the captured image. Each of the plurali...

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Bibliographic Details
Main Authors Gong, Yutao, Vengertsev, Dmitry, Gong, Jing, Eichmeyer, Seth A
Format Patent
LanguageEnglish
Published 05.03.2024
Subjects
BASIC ELECTRIC ELEMENTS
CALCULATING
COMPUTING
COUNTING
ELECTRIC SOLID STATE DEVICES NOT OTHERWISE PROVIDED FOR
ELECTRICITY
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES
MEASURING
PHYSICS
SEMICONDUCTOR DEVICES
TESTING
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Summary:An inspection system for determining wafer defects in semiconductor fabrication may include an image capturing device to capture a wafer image and a classification convolutional neural network (CNN) to determine a classification from a plurality of classes for the captured image. Each of the plurality of classes indicates a type of a defect in the wafer. The system may also include an encoder to encode to convert a training image into a feature vector; a cluster system to cluster the feature vector to generate soft labels for the training image; and a decoder to decode the feature vector into a re-generated image. The system may also include a classification system to determine a classification from the plurality of classes for the training image. The encoder and decoder may be formed from a CNN autoencoder. The classification CNN and the CNN autoencoder may each be a deep neural network.
Bibliography:Application Number: US202016925243