Evaluation of the Transfer Learning Models in Wafer Defects Classification
In a semiconductor industry, wafer defect detection has becoming ubiquitous. Various machine learning algorithms had been adopted to be the “brain” behind the machine for reliable, fast defect detection. Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had b...
Saved in:
Main Authors: | Jessnor Arif, Mat Jizat, Anwar, P. P. Abdul Majeed, Ahmad Fakhri, Ab. Nasir, Zahari, Taha, Yuen, Edmund, Lim, Shi Xuen |
---|---|
Format: | Conference or Workshop Item |
Language: | English |
Published: |
Springer Nature
2022
|
Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/36763/1/Evaluation%20of%20the%20Transfer%20Learning%20Models%20in%20Wafer%20Defects%20Classification%20%281%29.pdf http://umpir.ump.edu.my/id/eprint/36763/ https://doi.org/10.1007/978-981-33-4597-3_78 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Evaluation of the machine learning classifier in wafer defects classification
by: Jessnor Arif, Mat Jizat, et al.
Published: (2021) -
Effect of image compression using fast fourier transformation and discrete wavelet transformation on transfer learning wafer defect image classification
by: Jessnor Arif, Mat Jizat, et al.
Published: (2020) -
The Classification of Wafer Defects: A Support Vector Machine with Different DenseNet Transfer Learning Models Evaluation
by: Ismail, Mohd Khairuddin, et al.
Published: (2023) -
Wafer map defect pattern classification using deep learning model
by: Lim, Yu Pin
Published: (2023) -
Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning
by: Adriana, Ganum, et al.
Published: (2022)