Generative Adversarial Network-Based Fault Detection in Semiconductor Equipment with Class-Imbalanced Data

This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, result...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Sensors (Basel, Switzerland) Ročník 23; číslo 4; s. 1889
Hlavní autori: Choi, Jeong Eun, Seol, Da Hoon, Kim, Chan Young, Hong, Sang Jeen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 08.02.2023
MDPI
Predmet:
Algorithms
Control equipment
Etching equipment
fault detection
generative adversarial networks
Machine learning
optical emission spectroscopy
plasma etch
Plasma etching
Process controls
Sensors
Silicon wafers
Statistical methods
generative adversarial networks
machine learning
plasma etch
optical emission spectroscopy
fault detection
ISSN:1424-8220, 1424-8220
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This research proposes an application of generative adversarial networks (GANs) to solve the class imbalance problem in the fault detection and classification study of a plasma etching process. Small changes in the equipment part condition of the plasma equipment may cause an equipment fault, resulting in a process anomaly. Thus, fault detection in the semiconductor process is essential for success in advanced process control. Two datasets that assume faults of the mass flow controller (MFC) in equipment components were acquired using optical emission spectroscopy (OES) in the plasma etching process of a silicon trench: The abnormal process changed by the MFC is assumed to be faults, and the minority class of Case 1 is the normal class, and that of Case 2 is the abnormal class. In each case, additional minority class data were generated using GANs to compensate for the degradation of model training due to class-imbalanced data. Comparisons of five existing fault detection algorithms with the augmented datasets showed improved modeling performances. Generating a dataset for the minority group using GANs is beneficial for class imbalance problems of OES datasets in fault detection for the semiconductor plasma equipment.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23041889