Machine Learning for industrial applications: A comprehensive literature review

M Bertolini, D Mezzogori, M Neroni… - Expert Systems with …, 2021 - Elsevier
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …

A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry

M Abd Al Rahman, A Mousavi - Ieee Access, 2020 - ieeexplore.ieee.org
Electronics industry is one of the fastest evolving, innovative, and most competitive
industries. In order to meet the high consumption demands on electronics components …

Multimodal face-pose estimation with multitask manifold deep learning

C Hong, J Yu, J Zhang, X Jin… - IEEE transactions on …, 2018 - ieeexplore.ieee.org
Face-pose estimation aims at estimating the gazing direction with two-dimensional face
images. It gives important communicative information and visual saliency. However, it is …

Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition

J Wang, C Xu, Z Yang, J Zhang… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Defect pattern recognition (DPR) of wafer maps is critical for determining the root cause of
production defects, which can provide insights for the yield improvement in wafer foundries …

Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review

T Kim, K Behdinan - Journal of Intelligent Manufacturing, 2023 - Springer
With the high demand and sub-nanometer design for integrated circuits, surface defect
complexity and frequency for semiconductor wafers have increased; subsequently …

AdaBalGAN: An improved generative adversarial network with imbalanced learning for wafer defective pattern recognition

J Wang, Z Yang, J Zhang, Q Zhang… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Identification of the defective patterns of the wafer maps can provide insights for the quality
control in the semiconductor wafer fabrication systems (SWFSs). In real SWFSs, the …

Decision tree ensemble-based wafer map failure pattern recognition based on radon transform-based features

M Piao, CH Jin, JY Lee, JY Byun - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Wafer maps contain information about defects and clustered defects that form failure
patterns. Failure patterns exhibit the information related to defect generation mechanisms …

A light-weight neural network for wafer map classification based on data augmentation

TH Tsai, YC Lee - IEEE Transactions on Semiconductor …, 2020 - ieeexplore.ieee.org
In the semiconductor industry, the testing section has always played an important role. The
testing section often requires engineers to judge the defect, which wastes a lot of time and …

Wafer map defect detection and recognition using joint local and nonlocal linear discriminant analysis

J Yu, X Lu - IEEE Transactions on Semiconductor …, 2015 - ieeexplore.ieee.org
In semiconductor manufacturing processes, defect detection and recognition in wafer maps
have received increasing attention from semiconductor industry. The various defect patterns …

A novel DBSCAN-based defect pattern detection and classification framework for wafer bin map

CH Jin, HJ Na, M Piao, G Pok… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Defective die on a wafer map tend to cluster in distinguishable patterns, and such defect
patterns can provide crucial information to identify equipment problems or process failures in …