由於全球競爭的時代來臨，各家製造商為了能在產品市場搶得訂單，多會採取壓低售價的方式，期望能吸引顧客。廠商為了能有更便宜的銷售價格或是更高的獲利，勢必要對生產成本加以控制，除了尋找更便宜的原料，在生產上也會改善製程的良率，使得生產成本相對地降低。但是新產品由於是在剛發展階段，在生產的經驗和知識上並不充足。若是能夠將先前的生產經驗使用在新的製程上，對於加快良率提升的時間相信是有幫助的。
本論文研究一個新製程良率預測的問題。由於是新製程，在樣本數較小的情形下，為了能夠提升預測的精確度，利用先前製程的經驗，以整體趨勢擴散的技術建構新製程的經驗，產生與新製程相似的虛擬樣本來訓練預測模式。研究結果顯示，和其他的預測方法比較，本研究之方法，在精確性與穩定性都優於其他方法。

Because of the age of global competition is coming, every manufacturer would like to provide low price to attract customers and get order in the market. For providing lower selling price or getting higher profit, manufacturer must be control the production cost, not only finds cheaper material , but also improves the yield in manufacturing process to decrease the production cost relatively. In the beginning of new product of developing step, the experience and knowledge about manufacturing process is not sufficient. It is helpful for decreasing the researching time, if using the pre-manufacturing experience for new manufacturing process.
This thesis researches a problem about forecasting yield in new manufacturing process. Because of it is a new manufacturing process, in the situation that datasets is small, we use mega trend diffusion according to the experience about pre-manufacturing process to construct the experience about new manufacturing process. We make some virtual sample that is similar to the data in new manufacturing process to train forecast model. The result shows that, comparing to other forecast model, our model is better than others in accuracy and stability.

陳建誠, 在學習效應下大尺寸偏光板的良率預測, 國立成功大學工業與資訊管理研究所碩士論文, 2008
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