Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Khan, Mohd Rifat Accepted Dissertation Early SP24.pdf (3.37 MB)

Digital Accessibility Report

File List


Khan, Mohd Rifat 01-05-2024 - v1.pdf.accreport.html (8.27 KB)

ETD Abstract Container

Abstract Header

Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction

Khan, Mohd Rifat
http://orcid.org/0000-0003-4685-3001
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1704723606578934

Abstract Details

2024, Doctor of Philosophy (PhD), Ohio University, Mechanical and Systems Engineering (Engineering and Technology).
The world is currently experiencing a shortage of semiconductor chips. This shortage is affecting different industries that rely on electronic components that involve semiconductor chips to manufacture their products. Due to the shortage of chips, manufacturers are unable to complete the final assembly of their products, resulting in a delay in delivering the finished products to their customers. To address this issue, the US Congress passed the "Creating Helpful Incentives to Produce Semiconductors (CHIPS) and Science Act of 2022" on 9th August, 2022. This act aims to improve the competitiveness, innovation, and national security of the US. This dissertation focuses on addressing the chip shortage through the reduction of chronic semiconductor manufacturing process quality problems caused by wafer map surface defects. The proposed solution involves detecting mixed-type wafer map surface defect patterns using Ensemble Deformable Convolutional Neural Networks. The framework for defect detection proposed in this dissertation outperforms other machine learning models from literature, such as Conv-Pool-CNN, All-CNN, NIN-CNN, DCNN-v1, and DCNN-v2, in terms of F1-score. The proposed framework uses an industrial wafer map dataset (MixedWM38) from a semiconductor wafer manufacturing process to train the base models for the ensemble method. The results show that the proposed framework accurately identifies multi-pattern defects from the surface of wafer maps. This dissertation will contribute to advancing academic literature for the new field of detecting mixed-type defect patterns from the surface of wafer maps. Defects are indicators of process problems, and preventing quality defects in advance is the best approach to achieving positive yield. The efficient and accurate detection of wafer map mixed-type surface defect patterns is important for addressing chronic manufacturing process quality problems. The proposed framework can be used by semiconductor manufacturers for real-time defect detection on the manufacturing floor, enabling the identification of the relevant manufacturing process from where the defect originates and resolving process problems quickly. This dissertation will contribute to the advancement of semiconductor chip manufacturing by improving defect identification methods and reducing chronic manufacturing process quality issues that will lead to better yield outcomes, minimizing waste and damage. With the world facing chip shortages, improving the yield will contribute to solving the chip shortage crisis globally.
Tao Yuan (Advisor)
Gary Weckman (Committee Member)
Ashley Metcalf (Committee Member)
William Young (Committee Member)
Saeed Ghanbartehrani (Committee Member)
Omar Alhawari (Committee Member)
154 p.
Artificial Intelligence; Computer Science; Engineering; Industrial Engineering; Mathematics; Mechanical Engineering; Nanotechnology; Operations Research; Statistics; Systems Design
Semiconductor; Wafer Fabrication; Wafer Map; Ensemble Framework; Deformable Convolutional Neural Network; Mixed-type Surface Defect Pattern Detection; Chronic Process Quality Problem Reduction; CHIPS Shortage; CHIPS and Sciences Act; USA; American; Intel; Micron; National Security; MixedWM38; Artificial Intelligence; Machine Learning, Deep Learning

Recommended Citations

Citations

  • Khan, M. R. (2024). Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1704723606578934

    APA Style (7th edition)

  • Khan, Mohd Rifat. Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction. 2024. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1704723606578934.

    MLA Style (8th edition)

  • Khan, Mohd Rifat. "Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction." Doctoral dissertation, Ohio University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1704723606578934

    Chicago Manual of Style (17th edition)

ohiou1704723606578934
144
© 2024, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.