Browsing by Author "Horng, SC"

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A hybrid classification tree for products of complicated machines in flexible manufacturing systems
(2005)
Horng, SC
;
Lin, SY
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, we propose a Hybrid Classification Tree (HCT) to classify the products of complicated machines in flexible manufacturing systems. The HCT combines a proposed clustering algorithm with the Classification and Regression Tree (CART) to take the advantage of the constant property of control settings during any process step for a type of product. The proposed clustering algorithm split the data set into terminal clusters using splitting attributes based on a separation matrix and fuzzy rules. The terminal clusters which consist of the data of more than one product will be further classfied using the CART We have tested the HCT on the products of an ion implanter for the vast number of wafers of 26 recipes and compared the classification results and the computation time with the existing software See5 and CART The comparison results show that the HCT retains the classification accuracy of CART while saving 40% training time.
An effictive and efficient hierarchical fuzzy rule based classifier
(2003)
Tsai, CH
;
Lin, SY
;
Cheng, MH
;
Horng, SC
;
Liu, CH
In this paper, we propose a hierarchical fuzzy rule based classifier (HFRBC) for the classification problem with large number of classes and continuous attributes. A hierarchical clustering concept is introduced to achieve a finer fuzzy partition. Critical attributes are used to perform the cluster splitting and generate a cluster splitting tree. The effective attributes for the terminal clusters in the cluster splitting tree are picked so as to reduce the size of the fuzzy-rule set and hence reduce the computational complexity. The fuzzy rule generation procedures and classification procedures of the proposed HFRBC are simple and easily implemented. We have successfully applied the HFRBC to the classification problem of the working wafers in an ion implanter.
Reducing the overkills and retests in wafer testing process
(2003)
Horng, SC
;
Lin, SY
;
Cheng, MH
;
Yang, FY
;
Liu, CH
Reducing overkills is one of the main objectives in wafer testing process, however the major mean to prevent overkills is retest. In this paper, we formulate the problem of reducing overkills and retests as a stochastic optimization problem to determine optimal threshold values concerning the number of good dies and the number of bins in a lot and wafer to decide whether to go for a retest after a regular wafer probing. The considered stochastic optimization problem is an NP hard problem. We propose an Ordinal Optimization theory based two-level method to solve the problem for good enough threshold values to achieve lesser overkills and retests within a reasonable computational time. Applying to a case based on the true mean of bins of a real semiconductor product, the threshold values we obtained are the best among 1000 sets of randomly generated threshold values in the sense of lesser overkills under a tolerable retest rate.