A Study of Interaction Effect for High Dimensional Data with Application to Manufacturing Data of Multistage Process

  • 歐 嘉瑜

Student thesis: Master's Thesis


In statistical analysis the influences of both main effects and interaction effects are important to the response variable However some reasons such as variable filtering due to computational burden would make interaction terms (or sometimes called synergy factors) being masked No matter the field of industry statistics or bioinformatics the similar problem exists In semiconductor manufacturing industry huge investment is always consumed It also spends a lot of human and financial resources However after a great number of manufacturing procedure stages the final products often have defects or poor performance In order to reduce the loss caused by this situation and improve the products’ quality collection and analysis of the historical data of work in process (WIP) have become a trend In this thesis the aim is to find out one or some tools that would affect yield by using some statistical and data mining methods and these result can help to find out the possible root causes of the defect products The tools used in stages would be recorded during the manufacturing process For the purpose of finding the suspected tools (especially for the interactions or synergy factors) Dynamic Bayesian Network (Dean and Kanazawa 1989) and Learned Pattern Similarity (Baydogan and Runger 2015) are considered in this thesis At the end the results of these methods are compared with the traditional data mining strategies One of the major contributions of this research is to develop a framework for finding suspected tools with Dynamic Bayesian Network The assumption of the first order Markovian with fixed transition probability is relieved by using this framework Also a proposed approach which based on the concept of Learned Pattern Similarity is introduced The result of these approaches identify the used tools which could reduce the yield rates
Date of Award2016 Aug 1
Original languageEnglish
SupervisorShuen-Lin Jeng (Supervisor)

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