Recent advances in surface defect inspection of industrial products using deep learning techniques

X Zheng, S Zheng, Y Kong, J Chen - The International Journal of …, 2021 - Springer
Manual surface inspection methods performed by quality inspectors do not satisfy the
continuously increasing quality standards of industrial manufacturing processes. Machine …

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 …

Wafer map defect pattern classification and image retrieval using convolutional neural network

T Nakazawa, DV Kulkarni - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Wafer maps provide important information for engineers in identifying root causes of die
failures during semiconductor manufacturing processes. We present a method for wafer map …

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 …

A deep convolutional neural network for wafer defect identification on an imbalanced dataset in semiconductor manufacturing processes

M Saqlain, Q Abbas, JY Lee - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Wafer maps contain information about various defect patterns on the wafer surface and
automatic classification of these defects plays a vital role to find their root causes …

A voting ensemble classifier for wafer map defect patterns identification in semiconductor manufacturing

M Saqlain, B Jargalsaikhan… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
A wafer map contains a graphical representation of the locations about defect pattern on the
semiconductor wafer, which can provide useful information for quality engineers. Various …

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 …

Anomaly detection and segmentation for wafer defect patterns using deep convolutional encoder–decoder neural network architectures in semiconductor …

T Nakazawa, DV Kulkarni - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Abnormal defect pattern detection plays a key role in preventing yield loss excursion events
for the semiconductor manufacturing. We present a method for detecting and segmenting …

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 …