Using Supervised Artificial Neural Networks for Die Defect Detection
碩士 === 國立交通大學 === 工業工程與管理系 === 89 === Defects commonly occur on the surface of a wafer during production owing to carelessness of the operator, poor quality of equipment and inadequate environment. Such defects negatively impact subsequent operations. To maintain the die quality, factorie...
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ndltd-TW-089NCTU00310112016-01-29T04:27:57Z http://ndltd.ncl.edu.tw/handle/01118440805673560946 Using Supervised Artificial Neural Networks for Die Defect Detection 應用監督式類神經網路於晶粒表面缺陷辨識之研究 Ke, Chir-mour 柯岐謀 碩士 國立交通大學 工業工程與管理系 89 Defects commonly occur on the surface of a wafer during production owing to carelessness of the operator, poor quality of equipment and inadequate environment. Such defects negatively impact subsequent operations. To maintain the die quality, factories normally visually inspect dies after wafer sawing, subsequently leading to a significant amount of manpower, material resources and operation area and ultimately making it extremely difficult to maintain the inspection quality. This research presents an effective procedure capable of detecting die defects by using supervised neural networks. A case study involving the wafer production demonstrates the effectiveness and practicability of the proposed approach. Su, Chao-Ton 蘇朝墩 2001 學位論文 ; thesis 44 zh-TW |
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碩士 === 國立交通大學 === 工業工程與管理系 === 89 === Defects commonly occur on the surface of a wafer during production owing to carelessness of the operator, poor quality of equipment and inadequate environment. Such defects negatively impact subsequent operations. To maintain the die quality, factories normally visually inspect dies after wafer sawing, subsequently leading to a significant amount of manpower, material resources and operation area and ultimately making it extremely difficult to maintain the inspection quality. This research presents an effective procedure capable of detecting die defects by using supervised neural networks. A case study involving the wafer production demonstrates the effectiveness and practicability of the proposed approach.
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author2 |
Su, Chao-Ton |
author_facet |
Su, Chao-Ton Ke, Chir-mour 柯岐謀 |
author |
Ke, Chir-mour 柯岐謀 |
spellingShingle |
Ke, Chir-mour 柯岐謀 Using Supervised Artificial Neural Networks for Die Defect Detection |
author_sort |
Ke, Chir-mour |
title |
Using Supervised Artificial Neural Networks for Die Defect Detection |
title_short |
Using Supervised Artificial Neural Networks for Die Defect Detection |
title_full |
Using Supervised Artificial Neural Networks for Die Defect Detection |
title_fullStr |
Using Supervised Artificial Neural Networks for Die Defect Detection |
title_full_unstemmed |
Using Supervised Artificial Neural Networks for Die Defect Detection |
title_sort |
using supervised artificial neural networks for die defect detection |
publishDate |
2001 |
url |
http://ndltd.ncl.edu.tw/handle/01118440805673560946 |
work_keys_str_mv |
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