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 keymanufacturing 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.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Artificial Intelligence