Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning

In the ever-evolving landscape of the semiconductor industry, the integrity of Integrated Circuits (ICs) is critical to technological efficacy and advancement. As such, ensuring the quality of ICs is a high-stakes endeavour where accuracy and precision are paramount. This project introduces a compre...

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Main Author: Yaw, Qian Hui
Other Authors: Gwee Bah Hwee
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/177344
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1773442024-05-31T15:44:24Z Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning Yaw, Qian Hui Gwee Bah Hwee School of Electrical and Electronic Engineering ebhgwee@ntu.edu.sg Engineering IC image classification In the ever-evolving landscape of the semiconductor industry, the integrity of Integrated Circuits (ICs) is critical to technological efficacy and advancement. As such, ensuring the quality of ICs is a high-stakes endeavour where accuracy and precision are paramount. This project introduces a comprehensive two-tiered machine learning system, leveraging the robust capabilities of Convolutional Neural Networks (CNNs) to automate the intricate process of defect detection and quality assessment of IC images. Demonstrating an average defect detection accuracy of 96.66% across the Cyclone 3 family of IC types, the system marks a significant advancement over traditional manual inspection methods. Additionally, the Image Quality Assessment (IQA) phase of the system attains an overall accuracy of 78.77%, which is a testament to the system’s prowess in distinguishing between 'low priority defect,' 'minor defect,' and 'okay' classifications. Through the integration of extensive data processing, rigorous model training, and critical validation processes, the system underscores a revolutionary approach to quality control in semiconductor manufacturing, delivering on the promise of enhanced efficiency and reliability. Bachelor's degree 2024-05-28T00:38:22Z 2024-05-28T00:38:22Z 2024 Final Year Project (FYP) Yaw, Q. H. (2024). Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177344 https://hdl.handle.net/10356/177344 en A2079-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
IC image classification
spellingShingle Engineering
IC image classification
Yaw, Qian Hui
Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
description In the ever-evolving landscape of the semiconductor industry, the integrity of Integrated Circuits (ICs) is critical to technological efficacy and advancement. As such, ensuring the quality of ICs is a high-stakes endeavour where accuracy and precision are paramount. This project introduces a comprehensive two-tiered machine learning system, leveraging the robust capabilities of Convolutional Neural Networks (CNNs) to automate the intricate process of defect detection and quality assessment of IC images. Demonstrating an average defect detection accuracy of 96.66% across the Cyclone 3 family of IC types, the system marks a significant advancement over traditional manual inspection methods. Additionally, the Image Quality Assessment (IQA) phase of the system attains an overall accuracy of 78.77%, which is a testament to the system’s prowess in distinguishing between 'low priority defect,' 'minor defect,' and 'okay' classifications. Through the integration of extensive data processing, rigorous model training, and critical validation processes, the system underscores a revolutionary approach to quality control in semiconductor manufacturing, delivering on the promise of enhanced efficiency and reliability.
author2 Gwee Bah Hwee
author_facet Gwee Bah Hwee
Yaw, Qian Hui
format Final Year Project
author Yaw, Qian Hui
author_sort Yaw, Qian Hui
title Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
title_short Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
title_full Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
title_fullStr Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
title_full_unstemmed Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
title_sort taxonomy, clustering, and classification of integrated circuit image datasets for deep learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177344
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