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

33

All available object's versions:

https://ribes-54.man.poznan.pl/publication/2519

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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

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

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(+48) 5128 696

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biblioteka@il-pib.pl

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