Wyszukiwanie zaawansowane ?

Obiekt

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

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:

34

Wszystkie dostępne wersje tego obiektu:

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

Wyświetl opis w formacie RDF:

RDF

Wyświetl opis w formacie OAI-PMH:

OAI-PMH

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

Obiekty Podobne

2024, nr 3, JTIT-artykuły
2024, nr 3, JTIT-artykuły
A Generalized Learning Approach to Deep Neural Networks, Journal of Telecommunications and Information Technology, 2024, nr 3

Ponti, Francesca Frezza, Fabrizio Simeoni, Patrizio Parisi, Raffaele

2024, nr 3
artykuł
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
Więcej
×

Cytowanie

Styl cytowania:

Dane kontaktowe

Adres

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

Telefon

(+48) 5128 696

E-Mail

biblioteka@il-pib.pl

Odwiedź nas!

Ta strona wykorzystuje pliki 'cookies'. Więcej informacji