Paper Info
Title
Fault Detection Prediction Using A Deep Belief Network-Based Multi-Classifier In The Semiconductor Manufacturing Process
Abstract
The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is needed to determine whether a pass/fail test is accurate or not. In this paper, a deep belief network (DBN)-based multi-classifier is proposed for fault detection prediction in the semiconductor manufacturing process. The proposed method consists of two phases: The first phase is a data pre-processing phase in which features required for semiconductor data sets are extracted and the imbalance problem is solved. The second phase is to configure the multi-DBN using selected features. A DBN classifier is created for each feature and, finally, fault detection prediction is performed. The proposed method showed excellent performance and can be used in the semiconductor manufacturing process efficiently.
Year
DOI
Venue
2019
10.1142/S0218194019400126
INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
Keywords
Field
DocType
Semiconductor manufacturing process, fault detection prediction, DBN, multi-classifier
Data mining,Wafer,Fault detection and isolation,Computer science,Semiconductor device fabrication,Deep belief network,Classifier (linguistics),Semiconductor industry,Reliability engineering
Journal
Volume
Issue
ISSN
29
8
0218-1940
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jaekwon Kim116427.56
Jong Sik Lee27418.95
Young Shin Han300.34