| Summary: | 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.
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