Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction

碩士 === 國立臺灣科技大學 === 工業管理系 === 106 === During the assembly process of LED components, die bonding and wire bonding are the key factors affecting the defective rate. When the die bonding and wire bonding machine work, the engineers will adjust the parameters based on their experience. Meanwhile, the e...

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Main Authors:	OU YANG,CHIN HUI, 歐陽志暉
Other Authors:	Chao Ou-Yang
Format:	Others
Language:	zh-TW
Published:	2018
Online Access:	http://ndltd.ncl.edu.tw/handle/u4q3y4

id	ndltd-TW-106NTUS5041071
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spelling	ndltd-TW-106NTUS50410712019-05-16T00:59:40Z http://ndltd.ncl.edu.tw/handle/u4q3y4 Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction 運用決策樹和長短期記憶遞迴神經網路協助固晶打線機台參數調整與品質預測 OU YANG,CHIN HUI 歐陽志暉碩士國立臺灣科技大學工業管理系 106 During the assembly process of LED components, die bonding and wire bonding are the key factors affecting the defective rate. When the die bonding and wire bonding machine work, the engineers will adjust the parameters based on their experience. Meanwhile, the equipment wouldn’t be remained functioning until products passed through quality inspections. It is inefficient for tuning parameters and quality inspection. Therefore, the research utilizes the parameters of machine by a manufacturer. It dependents on their relationship and Constructs a Long Short-term Memory (LSTM) Recurrent Neural Network Prediction Model. During the process, the research also combines the information by engineers, trying to construct the new features for the prediction model. Finally, it improved the overall model of prediction rate. In the die bonding data, the die bonding condition and the height of the Silver Epoxy were recorded on each parameter of the machine. The relationship between the height of the silver Epoxy and the status of the die bonding is observed. We integrated the information into the decision tree and its branches are used as well. The result assists the engineer to adjust the parameters. The approach of this study can not only apply to the LED manufacturing process but also apply to similar area based on the same concept. It can improve the efficiency of a production line. Chao Ou-Yang 歐陽超 2018 學位論文 ; thesis 64 zh-TW
collection	NDLTD
language	zh-TW
format	Others
sources	NDLTD
description	碩士 === 國立臺灣科技大學 === 工業管理系 === 106 === During the assembly process of LED components, die bonding and wire bonding are the key factors affecting the defective rate. When the die bonding and wire bonding machine work, the engineers will adjust the parameters based on their experience. Meanwhile, the equipment wouldn’t be remained functioning until products passed through quality inspections. It is inefficient for tuning parameters and quality inspection. Therefore, the research utilizes the parameters of machine by a manufacturer. It dependents on their relationship and Constructs a Long Short-term Memory (LSTM) Recurrent Neural Network Prediction Model. During the process, the research also combines the information by engineers, trying to construct the new features for the prediction model. Finally, it improved the overall model of prediction rate. In the die bonding data, the die bonding condition and the height of the Silver Epoxy were recorded on each parameter of the machine. The relationship between the height of the silver Epoxy and the status of the die bonding is observed. We integrated the information into the decision tree and its branches are used as well. The result assists the engineer to adjust the parameters. The approach of this study can not only apply to the LED manufacturing process but also apply to similar area based on the same concept. It can improve the efficiency of a production line.
author2	Chao Ou-Yang
author_facet	Chao Ou-Yang OU YANG,CHIN HUI 歐陽志暉
author	OU YANG,CHIN HUI 歐陽志暉
spellingShingle	OU YANG,CHIN HUI 歐陽志暉 Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
author_sort	OU YANG,CHIN HUI
title	Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
title_short	Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
title_full	Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
title_fullStr	Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
title_full_unstemmed	Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
title_sort	using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction
publishDate	2018
url	http://ndltd.ncl.edu.tw/handle/u4q3y4
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Using decision tree and long short-term memory recurrent neural networks to assist in the adjustment parameters of die/wire bonding and quality prediction

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