Title: Estimating and incorporating the effects of a future QE project into a semiconductor yield learning model with a fuzzy set approach
Authors: Toly Chen
Addresses: Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City 407, Taiwan
Abstract: Yield prediction is a very important task to a semiconductor manufacturing factory. However, it is difficult because yield improvement is a learning process, and the uncertainty and variation inherent in the learning process are not easy to consider. In addition, the competition in the semiconductor industry is becoming more and more fierce, which significantly distorts the learning process of yield improvement. For example, some managerial actions (including increasing the release frequency and executing a quality-engineering project) might be taken to accelerate yield learning at various stages to prevent the product from losing competitiveness. The effects of such actions might be unstable, but still have to be estimated and then incorporated into the yield learning model. For this purpose, a fuzzy set approach is proposed in this study. At first, the fuzzy Delphi method is applied to aggregate the judgement results by multiple experts about the effects of a managerial action. Subsequently, a subjective correction function is designed to incorporate the aggregation result into Chen and Wang|s (1999) fuzzy yield learning model. To evaluate the effectiveness of the proposed methodology, it has been applied to the data of two random-access-memory products. [Received 02 July 2008; Accepted 06 October 2008]
Keywords: fuzzy Delphi method; correction function; semiconductor manufacturing; quality engineering; yield learning; fuzzy sets; fuzzy logic; yield prediction; modelling.
European Journal of Industrial Engineering, 2009 Vol.3 No.2, pp.207 - 226
Published online: 02 Mar 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article