Browsing by Author "Hsieh, KL"

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Incorporating process capability index and quality loss function into analyzing the process capability for qualitative data
(2006-02-01)
Hsieh, KL
;
Tong, LI
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability analysis (PCA) is frequently employed to evaluate a product or a process if it can meet the customer's requirement. In general, process capability analysis can be represented by using the process capability index (PCI). Until now, the PCI was frequently used for processes with quantitative characteristics. However, for process quality with the qualitative characteristic, the data's type and single specification caused limitations of using the PCI. When the product can not meet the target, even if it lies in the specified range, it should lead to the corresponding quality loss. Taguchi developed a quadratic quality loss function (QLF) to address such issues. In this study, we intend to construct a measurable index which incorporates the PCI philosophy and QLF concept to analyze the process capability with the consideration of the qualitative response data. The manufacturers can not only employ the proposed index to self-assess the process capability, but they also can make comparisons with the other competitors .
Manufacturing performance evaluation for IC products
(2006-03-01)
Hsieh, KL
;
Tong, LI
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
As we known, the product diversity and complexity in the production line will gradually increase. When the multiple products were alternately produced at the same line, the manufacturing performance will be difficult to evaluate. In particular, traditional process capability analysis and related process capability indexes cannot be directly employed to the IC manufacturing process. As we know, the yield has a direct effect on the manufacturing cost. Hence, yield is frequently used by most IC manufacturers to evaluate manufacturing performance. The diversity of function will become another analytic consideration due to that the component density, wafer area and product complexity of an IC product rapidly increase. Hence, the diversity of function can be regarded as the evaluated factor. Additionally, the defects on a wafer will begin to cluster as the wafer area gradually increases. Therefore, only using the yield to represent manufacturing performance may not lead to an appropriate judgment. In particular, only using the yield to evaluate the process's stability and the product's maturity can not provide a meaningful resolution. The primary reason is that the inherent features in the processes or products are not included into analyzing. For instance, even though the defect count, defect size and defect distribution are the same, the yield loss of the complicate manufactured product will be less than that of the simple manufactured product. In this study, we propose a simple performance evaluation index to assess the manufacturing performance in the IC manufacturing industry. This evaluation index is constructed according to a modified Poisson yield model, and the related parameters regarding the process or product (e.g., the minimum linewidth, the area of a die, the number of manufactured process or layer, the degree of defect clustering, and so on.) are taken into consideration. In addition, an integrated evaluation procedure is also suggested to evaluate the performance of the manufacturing of multiple IC products. According to the result obtained from the illustrative example, the index and the procedure can overcome the drawback of separately using yield or defect count in the analysis. The rationality and the feasibility of the proposed evaluated index and the procedure can be verified by demonstrating the illustrative example.
Optimization of a multi-response problem in Taguchi's dynamic system
(2005-12-01)
Hsieh, KL
;
Tong, LI
;
Chiu, HP
;
Yeh, HY
;
工業工程與管理學系
Taguchi method was known as an off-line quality control methodology to be used in many industries. Until now, most applications only focus on optimizing a single-response in a static system. Furthermore, due to the increasing complexity of the product design, more than one quality characteristic must be considered simultaneously to improve the production quality. Therefore, there are several studies to address the multi-response problem. In order to satisfy the requirements of the production's design, optimization of a dynamic system been mentioned by Taguchi has received more attentions in the recent years. Hence, optimizing a multi-response problem in a dynamic system becomes an important issue to address the quality improvement. This study proposes a procedure utilizing the statistic regression analysis and desirability function to optimize the multi-response problem with Taguchi's dynamic system consideration. Firstly, the regression analysis is employed to screen out the control factors significantly affecting the quality variation, and the adjustment factors significantly affecting the sensitivity of a Taguchi's dynamic system. Then, the desirability function will be applied to optimize such a multi-response problem. Finally, the effectiveness of the proposed procedure will be demonstrated by an example of a biological reduction of ethyl acetoacetate process experiment project at the Union Chemical Laboratories of the Industrial Technology Research Institute in Taiwan. (c) 2005 Elsevier Ltd. All rights reserved.
Optimization of multiple quality responses involving qualitative and quantitative characteristics in IC manufacturing using neural networks
(2001-08-01)
Hsieh, KL
;
Tong, LI
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The optimization of product or process quality profoundly influences a manufacturer. Most studies have focused primarily on optimizing a quantitative (or qualitative) quality response, while others have concentrated on optimizing multiple quantitative quality responses. However, optimizing multiple responses involving both qualitative and quantitative characteristics have scarcely been mentioned, largely owing to the inability to directly apply conventional optimization techniques. In this study, we present a novel approach based on artificial neural networks (ANNs) to simultaneously optimize multiple responses including both qualitative and quantitative quality characteristics. Two neural networks are constructed: one for determining the ideal parameter settings and the other for estimating the values of the multiple quality characteristics. In addition, a numerical example from an ion implantation process employed by a Taiwan IC fabrication manufacturer demonstrates the proposed approach's effectiveness. (C) 2001 Published by Elsevier Science B.V.