AIoT for sustainable manufacturing: Overview, challenges, and opportunities
The integration of IoT and AI has gained significant attention as an emerging means to
digitize manufacturing industries and drive sustainability in the context of Industry 4.0. In …
digitize manufacturing industries and drive sustainability in the context of Industry 4.0. In …
A review of current machine learning techniques used in manufacturing diagnosis
TT Ademujimi, MP Brundage, VV Prabhu - … 3-7, 2017, Proceedings, Part I, 2017 - Springer
Artificial intelligence applications are increasing due to advances in data collection systems,
algorithms, and affordability of computing power. Within the manufacturing industry, machine …
algorithms, and affordability of computing power. Within the manufacturing industry, machine …
A deep learning model for robust wafer fault monitoring with sensor measurement noise
Standard fault detection and classification (FDC) models detect wafer faults by extracting
features useful for fault detection from time-indexed measurements of the equipment …
features useful for fault detection from time-indexed measurements of the equipment …
Anomaly detection approaches for semiconductor manufacturing
Smart production monitoring is a crucial activity in advanced manufacturing for quality,
control and maintenance purposes. Advanced Monitoring Systems aim to detect anomalies …
control and maintenance purposes. Advanced Monitoring Systems aim to detect anomalies …
Anomaly detection through on-line isolation forest: An application to plasma etching
Advanced Monitoring Systems are fundamental in advanced manufacturing for control,
quality and maintenance purposes. Nowadays, with the increasing availability of data in …
quality and maintenance purposes. Nowadays, with the increasing availability of data in …
Science-based, data-driven developments in plasma processing for material synthesis and device-integration technologies
M Kambara, S Kawaguchi, HJ Lee… - Japanese Journal of …, 2022 - iopscience.iop.org
Low-temperature plasma-processing technologies are essential for material synthesis and
device fabrication. Not only the utilization but also the development of plasma-related …
device fabrication. Not only the utilization but also the development of plasma-related …
Machine learning-based process-level fault detection and part-level fault classification in semiconductor etch equipment
SH Kim, CY Kim, DH Seol, JE Choi… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In the semiconductor manufacturing, which consists of significantly precise and diverse unit
processes, minute defects can cause significantly large risk, which is directly related to the …
processes, minute defects can cause significantly large risk, which is directly related to the …
Artificial immune system for fault detection and classification of semiconductor equipment
Semiconductor manufacturing comprises hundreds of consecutive unit processes. A single
misprocess could jeopardize the whole manufacturing process. In current manufacturing …
misprocess could jeopardize the whole manufacturing process. In current manufacturing …
Use of plasma information in machine-learning-based fault detection and classification for advanced equipment control
DH Kim, SJ Hong - IEEE Transactions on Semiconductor …, 2021 - ieeexplore.ieee.org
For advanced equipment control, two schemata of real-time fault detection were performed
using machine learning algorithms in silicon etching in SF 6/O 2/Ar plasma. Fault detection …
using machine learning algorithms in silicon etching in SF 6/O 2/Ar plasma. Fault detection …
Support weighted ensemble model for open set recognition of wafer map defects
Wafer defect maps have different generation mechanisms according to the defect pattern,
and automatic classification of wafer maps is therefore critical to reveal the root cause of the …
and automatic classification of wafer maps is therefore critical to reveal the root cause of the …