國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction/ Mohd Rifat Khan.
作者:	Khan, Mohd Rifat,
面頁冊數:	1 electronic resource (155 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31359524
ISBN:	9798382786049

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

Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction [electronic resource] /Mohd Rifat Khan. - 1 electronic resource (155 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

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.

English

ISBN: 9798382786049Subjects--Topical Terms:

557493
Mechanical engineering.
Subjects--Index Terms:

Semiconductor

Mixed Type Wafer Defect Pattern Recognition Using Ensemble Deformable Convolutional Neural Networks for Chronic Manufacturing Process Quality Problems Reduction
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520 # $a 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.
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