Advanced search ?

Object

Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1
Download

Title: Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1

Creator:

Fauzi, Ahmad Hadinata ; Iskandar, D. N. F. Awang ; Ganum, Adriana ; Chin, Lim Phei

Date:

2022, nr 1

Resource Type:

artykuł

Description:

Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of MobileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852 ; oai:bc.itl.waw.pl:2213

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności

Subject and keywords:

machine learning neural network wafer imperfection identification automated optical inspection

Object collections:

Last modified:

Mar 11, 2024

In our library since:

Apr 26, 2022

Number of object content hits:

23

All available object's versions:

https://bc.itl.waw.pl/publication/2519

Show description in RDF format:

RDF

Show description in OAI-PMH format:

OAI-PMH

Edition name Date
Chin, Lim Phei, Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1 Mar 11, 2024

Objects Similar
The Application of Neural Networks to the Process of Gaining and Consolidating the Knowledge, Journal of Telecommunications and Information Technology, 2012, nr 1

Plichta, Anna

2012, nr 1
artykuł
Network Traffic Classification in an NFV Environment using Supervised ML Algorithms, Journal of Telecommunications and Information Technology, 2021, nr 3

Latkoski, Pero Ilievski, Gjorgji

2021, nr 3
artykuł
Manipulation of compressed data using MPEG-7 low level audio descriptors, Journal of Telecommunications and Information Technology, 2003, nr 2

Harders, Nick Perrow, Shane Stirling, David Lukasiak, Jason

2003, nr 2
artykuł
Contextual probability, Journal of Telecommunications and Information Technology, 2003, nr 3

Wang, Hui

2003, nr 3
artykuł
2023, nr 4, JTIT-artykuły
2023, nr 4, JTIT-artykuły
Designing Smart Antennas Using Machine Learning Algorithms, Journal of Telecommunications and Information Technology, 2023, nr 4

Samantaray, Barsa Das, Kunal Kumar Roy, Jibendu Sekhar

2023
artykuł
Evaluation of Radio Channel Utility using Epsilon-Greedy Action Selection, Journal of Telecommunications and Information Technology, 2021, nr 3

Malon, Krzysztof

2021, nr 3
artykuł
Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features, Journal of Telecommunications and Information Technology, 2022, nr 4

Dzwonkowski, Mariusz Czaplewski, Bartosz Panas, Damian

2022, nr 4
artykuł
Markov Decision Process based Model for Performance Analysis an Intrusion Detection System in IoT Networks, Journal of Telecommunications and Information Technology, 2021, nr 3

Kalnoor, Gauri S, Gowrishankar

2021, nr 3
artykuł
More
×

Citation

Citation style:

Contact details

Address

Instytut Łączności – Państwowy Instytut Badawczy ul. Szachowa 1 04-894 Warszawa

Phone

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Visit us!

This page uses 'cookies'. More information