Ditapis dengan
Statistical Comparison of Fault Detection Models for Semiconductor Manufactur…
Abstract—A variety of statistical and data-mining techniques have been developed for the fault detection (FD) modeling of semiconductor manufacturing processes over the past three decades. However, few studies have analyzed which models are adequate for different types of fault data. In this paper, we define a FD model as an algorithm combining feature extraction, feature selection, and class…
- Edisi
- VOL. 28, NO. 1, FEBRUARY 2015
- ISBN/ISSN
- 0894-6507
- Deskripsi Fisik
- IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 1, FEBRUARY 2015
- Judul Seri
- IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 1, FEBRUARY 2015
- No. Panggil
- -
An Incremental Clustering-Based Fault Detection Algorithm for Class-Imbalance…
Abstract Training fault detection model requires advanced data-mining algorithms when the growth rate of the process data is notably high and normal-class data overwhelm fault-class data in number. Most standard classification algorithms, such as support vector machines (SVMs), can handle moderate sizes of training data and assume balanced class distributions. When the class sizes are highly …
- Edisi
- VOL. 28, NO. 3, AUGUST 2015
- ISBN/ISSN
- 0894-6507
- Deskripsi Fisik
- IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 3, AUGUST 2015
- Judul Seri
- IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, VOL. 28, NO. 3, AUGUST 2015
- No. Panggil
- -
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