期刊
TECHNOMETRICS
卷 57, 期 3, 页码 320-331出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/00401706.2015.1029079
关键词
Data fusion; Model selection; Nonnegative garrote; Quantitative and qualitative quality control
资金
- National Science Foundation [CMMI-1435996, CMMI-1233571]
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1435996] Funding Source: National Science Foundation
A manufacturing system with both quantitative and qualitative (QQ) quality responses (as a QQ system) is widely encountered in many cases. For example, in a lapping process of the semiconductor manufacturing, the quality of wafer's geometrical characteristics is often measured by the total thickness variation as a quantitative response and the conformity of site total indicator reading as a binary qualitative response. The QQ responses are closely associated with each other in a QQ system, but current methodologies often model the two types of quality responses separately. This article presents a novel modeling approach, called QQ models, to jointly model the QQ responses through a constrained likelihood estimation. The QQ models can jointly select significant predictors by incorporating inherent features of QQ systems, leading to accurate variable selection and prediction. Both simulation studies and a case study in a lapping process are used to evaluate the performance of the proposed method. Supplementary materials to this article are available online.
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