Automatic root cause analysis in manufacturing: an overview & conceptualization

E e Oliveira, VL Miguéis, JL Borges - Journal of Intelligent Manufacturing, 2023 - Springer
Root cause analysis (RCA) is the process through which we find the true cause of a
problem. It is a crucial process in manufacturing, as only after finding the root cause and …

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 …

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 …

Data-driven approach for fault detection and diagnostic in semiconductor manufacturing

SKS Fan, CY Hsu, DM Tsai, F He… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Fault detection and classification (FDC) is important for semiconductor manufacturing to
monitor equipment's condition and examine the potential cause of the fault. Each equipment …

A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge

Y Bai, J Xie, D Wang, W Zhang, C Li - Computers & Industrial Engineering, 2021 - Elsevier
Manufacturing quality prediction is one of the significant concerns in modern enterprise
production management, which provides data support for reliability assessment and …

AI‐Driven Digital Twin Model for Reliable Lithium‐Ion Battery Discharge Capacity Predictions

P Nair, V Vakharia, M Shah, Y Kumar… - … Journal of Intelligent …, 2024 - Wiley Online Library
The present study proposes a novel method for predicting the discharge capabilities of
lithium‐ion (Li‐ion) batteries using a digital twin model in practice. By combining cutting …

Fault detection and diagnosis using self-attentive convolutional neural networks for variable-length sensor data in semiconductor manufacturing

E Kim, S Cho, B Lee, M Cho - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Nowadays, more attention has been placed on cost reductions and yield enhancement in
the semiconductor industry. During the manufacturing process, a considerable amount of …

Multi-layer parallel transformer model for detecting product quality issues and locating anomalies based on multiple time‑series process data in Industry 4.0

J Leng, Z Lin, M Zhou, Q Liu, P Zheng, Z Liu… - Journal of Manufacturing …, 2023 - Elsevier
Smart manufacturing systems typically consist of multiple machines with different processing
durations. The continuous monitoring of these machines produces multiple time-series …

A fast ramp-up framework for wafer yield improvement in semiconductor manufacturing systems

HW Xu, QH Zhang, YN Sun, QL Chen, W Qin… - Journal of Manufacturing …, 2024 - Elsevier
Abstracts Wafer yield is crucial for assessing semiconductor fabrication enterprises' stability
and technological maturity. Quickly achieving the yield ramp-up of new products and timely …

A review of data mining with big data towards its applications in the electronics industry

S Lv, H Kim, B Zheng, H Jin - Applied Sciences, 2018 - mdpi.com
Featured Application This review not only benefits researchers to develop strong research
themes and identify gaps in the field but also helps practitioners for DM and Big Data …