Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

Unsupervised Pre-Training of Imbalanced Data for Identification of Wafer Map Defect Patterns

Visual defect inspection and classification are significant steps of most manufacturing processes in the semiconductor and electronics industries. Known and unknown defects on wafer maps tend to cluster, and these spatial patterns provide valuable process information for supporting manufacturing in...

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Bibliographic Details
Main Authors: Ho Sun Shon, Erdenebileg Batbaatar, Wan-Sup Cho, Seong Gon Choi
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Classification
convolutional variational autoencoder
deep learning
imbalanced data
neural network
unsupervised pre-training
Online Access:https://ieeexplore.ieee.org/document/9385104/

Internet

https://ieeexplore.ieee.org/document/9385104/

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