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

Autor:

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

Data wydania:

2022, nr 1

Typ zasobu:

artykuł

Opis:

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.

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Format:

application/pdf

Identyfikator zasobu:

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

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Prawa:

Biblioteka Naukowa Instytutu Łączności

Temat i słowa kluczowe:

machine learning neural network wafer imperfection identification automated optical inspection

Kolekcje, do których przypisany jest obiekt:

Data ostatniej modyfikacji:

11 mar 2024

Data dodania obiektu:

26 kwi 2022

Liczba wyświetleń treści obiektu:

23

Wszystkie dostępne wersje tego obiektu:

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

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Nazwa wydania Data
Chin, Lim Phei, Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning, Journal of Telecommunications and Information Technology, 2022, nr 1 11 mar 2024

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