Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes

Authors:
Der-Chiang Li;Yao-Hwei Fang;Chiao-Wen Liu;Cheng-Jung Juang
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC 70101;Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC 70101;Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC 70101;Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC 70101
Venue:
Journal of Intelligent Manufacturing
Year:
2012

Citing 9
Cited 2

Computational learning theory: an introduction

Computational learning theory: an introduction
Support-Vector Networks

Machine Learning
Improving support vector machine classifiers by modifying kernal functions

Neural Networks
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Credit rating analysis with support vector machines and neural networks: a market comparative study

Decision Support Systems - Special issue: Data mining for financial decision making
Utilize bootstrap in small data set learning for pilot run modeling of manufacturing systems

Expert Systems with Applications: An International Journal
Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Computers and Operations Research
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Expert Systems with Applications: An International Journal
Financial time series prediction using least squares support vector machines within the evidence framework

IEEE Transactions on Neural Networks

Probabilistic assessment of loss in revenue generation in demand-driven production

Journal of Intelligent Manufacturing
A new approach for manufacturing forecast problems with insufficient data: the case of TFT---LCDs

Journal of Intelligent Manufacturing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Polarizers are one of the key parts of Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD), and their production requires high material costs. How to reduce manufacturing costs is thus a key task in this highly competitive global market. The precise yield forecast model considering learning effects that is proposed in this work is believed to be an effective approach to reduce both the raw material input-cost and inventory cost of overproduction. Support vector regression (SVR) model is one of the commonly used approaches to forecast the yield trend. However, in the early manufacturing stages for a new product, an SVR model is usually sensitive and unstable because of the use of insufficient data. Faced with this problem, this research aims at enhancing the SVR model by using past manufacturing experience and virtual samples to estimate the yield trend model for pilot products. This paper proposes a novel Quadratic-Curve Diffusion (QCD) method, wherein we derive a quadratic yield function (QYF) of the new manufacturing process for each key manufacturing variable by utilizing past manufacturing experience; and then use the QYF to generate virtual samples to assist building the overall yield forecast model of the new manufacturing process. The results show that the proposed method is superior to the performance of other forecast and virtual sample generation models.