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A Novel Visual Fault Detection and Classification System for
  Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural
  Networks
v1v2v3v4v5v6 (latest)

A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

25 November 2019
Tobias Schlosser
Frederik Beuth
Michael Friedrich
Danny Kowerko
ArXiv (abs)PDFHTML

Papers citing "A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks"

2 / 2 papers shown
Title
Utilizing Generative Adversarial Networks for Image Data Augmentation
  and Classification of Semiconductor Wafer Dicing Induced Defects
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects
Zhining Hu
Tobias Schlosser
Michael Friedrich
André Luiz Vieira e Silva
Frederik Beuth
Danny Kowerko
52
3
0
24 Jul 2024
Improving Automated Visual Fault Detection by Combining a Biologically
  Plausible Model of Visual Attention with Deep Learning
Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning
Frederik Beuth
Tobias Schlosser
Michael Friedrich
Danny Kowerko
33
12
0
13 Feb 2021
1