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 …

[HTML][HTML] A review of data mining applications in semiconductor manufacturing

P Espadinha-Cruz, R Godina, EMG Rodrigues - Processes, 2021 - mdpi.com
For decades, industrial companies have been collecting and storing high amounts of data
with the aim of better controlling and managing their processes. However, this vast amount …

Industry 4.0 implementation: The relevance of sustainability and the potential social impact in a developing country

WC Satyro, CMVB de Almeida, MJA Pinto Jr Jr… - Journal of Cleaner …, 2022 - Elsevier
This study expands the technical approach that dominates the academic literature on
Industry 4.0, identifying relevant benefits and challenges for its implementation process …

Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing

CY Hsu, WC Liu - Journal of Intelligent Manufacturing, 2021 - Springer
The development of information technology and process technology have been enhanced
the rapid changes in high-tech products and smart manufacturing, specifications become …

Deep reinforcement learning for selecting demand forecast models to empower Industry 3.5 and an empirical study for a semiconductor component distributor

CF Chien, YS Lin, SK Lin - International Journal of Production …, 2020 - Taylor & Francis
A semiconductor distributor that plays a third-party role in the supply chain will buy diverse
components from different suppliers, warehouse and resell them to a number of electronics …

A systematic review of deep learning for silicon wafer defect recognition

U Batool, MI Shapiai, M Tahir, ZH Ismail… - IEEE …, 2021 - ieeexplore.ieee.org
Advancements in technology have made deep learning a hot research area, and we see its
applications in various fields. Its widespread use in silicon wafer defect recognition is …

Ensemble convolutional neural networks with weighted majority for wafer bin map pattern classification

CY Hsu, JC Chien - Journal of Intelligent Manufacturing, 2022 - Springer
Wafer bin maps (WBM) provides crucial information regarding process abnormalities and
facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies …

Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5

MT Nguyen, LH Truong, TT Tran, CF Chien - Computers & Industrial …, 2020 - Elsevier
Nowadays, the demand of camera surveillance systems (CSS) has been increasingly
adopted in various industries for smart manufacturing. However, the increase of utilizing …

Mixup-based classification of mixed-type defect patterns in wafer bin maps

W Shin, H Kahng, SB Kim - Computers & Industrial Engineering, 2022 - Elsevier
Wafer bin maps (WBMs) that exhibit systematic defect patterns provide clues for
identification of critical failures that occur during the wafer fabrication process. Proper …

Advanced quality control for probe precision forming to empower virtual vertical integration for semiconductor manufacturing

W Fu, CF Chien, CH Chen - Computers & Industrial Engineering, 2023 - Elsevier
Circuit probe is crucial for electrically testing for functional defects to determine the known
good dies before integrated circuit (IC) packaging. Semiconductor probe forming showing …