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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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/177344 |
| _version_ | 1826124277744664576 |
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| author | Yaw, Qian Hui |
| author2 | Gwee Bah Hwee |
| author_facet | Gwee Bah Hwee Yaw, Qian Hui |
| author_sort | Yaw, Qian Hui |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:18:02Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/177344 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:18:02Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering IC image classification Yaw, Qian Hui Taxonomy, clustering, and classification of integrated circuit image datasets for deep learning |
| title | 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_short | 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 |
| topic | Engineering IC image classification |
| url | https://hdl.handle.net/10356/177344 |
| work_keys_str_mv | AT yawqianhui taxonomyclusteringandclassificationofintegratedcircuitimagedatasetsfordeeplearning |