Using Artifical Neural Network for TFT-LCD Yield Prediction
| Autor: | Shih-Wei Chen, 陳仕偉 |
|---|---|
| Rok vydání: | 2012 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 100 Taiwan is the primary manufactured and produced country in TFT-LCD(Thin-Film Transistor Liquid-Crystal Display). Because the investment needs lots of cost in equipments and facilities produced of TFT-LCD, how it reduces manufactured time, keeping the quality, it is the necessary maintained requirement for enterprises to exhibit the production in market sooner than competitors. Yield is the correlated sample to reflect the whole technology and profit obtain in enterprises, so it is significant within many measured indicators. Simply speaking, yield is defined the percentage of good productions in all produced amount, and yield management is refer to produce a great number of data to integrated analysis, yield improvement, and yield prediction in the whole TFT-LCD making process. However, it would be the effective predicted model to define and clarify some aspects such as production materials, delivery time, and making problems, so yield prediction is gradually become the significant topic in enterprises. Through the above instructions, the study tries to present an effective and simple yield prediction model: it is based on TFT-LCD panel arrayed data, and operates Artificial Neural Back-Propagation Network having the characteristics such as learning, parallel computing and compatibility to develop the method of TFT-LCD yield prediction. In order to control the complexity of ANBPN architecture, the study will apply Principal Components Analysis and Stepwise Variable Selection Analysis to reduce input variable instability. A real case was presented to demonstrate the methodology and the result revealed that by stepwise variable selection of BPN can provide an acceptable for TFT-LCD yield prediction. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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