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A Bayesian Ranking Scheme for supporting cost-effective yield diagnosis services

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2 Author(s)
Chih-Min Fan ; Ind. Eng. & Manage. Dept., Yuan Ze Univ., Taoyuan, Taiwan ; Yun-Pei Lu

A Bayesian Ranking Scheme is proposed for the reliable diagnosis of various yield-loss factors induced in semiconductor manufacturing. The aim is to cope with three problems: (FICV) false identification due to confounding variables, (FISV) false identification due to suppressor variables, and (MISC) miss identification due to severe multicollinearity. The proposed scheme reuses both the results from knowledge-based and data-driven inference tools as input data, where the former resembles expert's knowledge on diagnosing pre-known yield-loss factors while the latter serves for exploring new yield-loss factors. Two successive stages with specific designs for yield diagnosis services are addressed: Bayesian Variable Selection for reliable model construction and Relative Importance Assessment for facilitating interpretations on model parameters. A simulation example is designed to demonstrate the usefulness of Bayesian Ranking Scheme on solving FICV, FISV and MISC problems.

Published in:

Automation Science and Engineering, 2009. CASE 2009. IEEE International Conference on

Date of Conference:

22-25 Aug. 2009