Quality monitoring in multistage manufacturing systems by using machine learning techniques

M Ismail, NA Mostafa, A El-Assal - Journal of Intelligent Manufacturing, 2022 - Springer
Manufacturing and production processes have become more complicated and usually
consist of multiple stages to meet customers' requirements. This poses big challenges for …

MI-MOTE: Multiple imputation-based minority oversampling technique for imbalanced and incomplete data classification

K Shin, J Han, S Kang - Information Sciences, 2021 - Elsevier
Class imbalance and data incompleteness problems occur simultaneously in many real-
world classification datasets, which negatively affects the training of classifiers. Given an …

Unsupervised pre-training of imbalanced data for identification of wafer map defect patterns

HS Shon, E Batbaatar, WS Cho, SG Choi - IEEE Access, 2021 - ieeexplore.ieee.org
Visual defect inspection and classification are significant steps of most manufacturing
processes in the semiconductor and electronics industries. Known and unknown defects on …

Machine learning-based techniques for fault diagnosis in the semiconductor manufacturing process: a comparative study

AA Nuhu, Q Zeeshan, B Safaei… - The Journal of …, 2023 - Springer
Industries are going through the fourth industrial revolution (Industry 4.0), where
technologies like the Industrial Internet of things, big data analytics, and machine learning …

Semi-GAN: An improved GAN-based missing data imputation method for the semiconductor industry

SY Lee, TP Connerton, YW Lee, D Kim, D Kim… - Ieee …, 2022 - ieeexplore.ieee.org
Complete data are required for the operation, maintenance, and detection of faults in
semiconductor equipment. Missing data occur frequently because of defects such as sensor …

Artificial immune system for fault detection and classification of semiconductor equipment

H Park, JE Choi, D Kim, SJ Hong - Electronics, 2021 - mdpi.com
Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single
misprocess could jeopardize the whole manufacturing process. In current manufacturing …

Interpretation of a deep analysis of speech imagery features extracted by a capsule neural network

JM Macías-Macías, JA Ramírez-Quintana… - Computers in Biology …, 2023 - Elsevier
Speech imagery has been successfully employed in develo** Brain–Computer Interfaces
because it is a novel mental strategy that generates brain activity more intuitively than …

Pruning Quantized Unsupervised Meta-Learning DegradingNet Solution for Industrial Equipment and Semiconductor Process Anomaly Detection and Prediction

YC Yu, SR Yang, SW Chuang, JT Chien, CY Lee - Applied Sciences, 2024 - mdpi.com
Machine-and deep-learning methods are used for industrial applications in prognostics and
health management (PHM) for semiconductor processing and equipment anomaly detection …

A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems

CM Carbery, R Woods… - Proceedings of the …, 2019 - journals.sagepub.com
Recent emphasis has been placed on improving the processes in manufacturing by
employing early detection or fault prediction within production lines. Whilst companies are …

Particle swarm optimization based deep learning ensemble for manufacturing processes

D Moldovan, I Anghel, T Cioara… - 2020 IEEE 16th …, 2020 - ieeexplore.ieee.org
Faults detection in semiconductor manufacturing processes is a challenging research
problem because the manufacturing processes are characterized by hundreds of different …