Browsing by Author "Chiang, TL"

Now showing 1 - 5 of 5

A neural network based information granulation approach to shorten the cellular phone test process
(2006-06-01)
Su, CT
;
Chen, LS
;
Chiang, TL
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In the cellular phone OEM/ODM industry, reducing test time and cost are crucial due to fierce competition, short product life cycle, and a low margin environment. Among the inspection processes, the radio frequency (RF) function test process requires more operation time than any other. Hence, manufacturers need an effective method to reduce the RF test items so that the inspection time can be reduced while maintaining the quality of the RF function test. However, traditional feature selection methods such as neural networks and genetic algorithm lead to a high level of Type II error in the situation of imbalanced data where the amount of good products is far greater than the defective products. In this study, we propose a neural network based information granulation approach to reduce the RF test items for the finished goods inspection process of a cellular phone. Implementation results show that the RF test items were significantly reduced, and that the inspection accuracy remains very close to that of the original testing process. In addition, the Type II errors decreased as well. (C) 2006 Elsevier B.V. All rights reserved.
Applying robust multi-response quality engineering for parameter selection using a novel neural-genetic algorithm
(2003-01-01)
Li, TS
;
Su, CT
;
Chiang, TL
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This study presents a neural-genetic algorithm to solve the selection problem of manufacturing process parameters. The proposed algorithm is a combination of artificial neural network (ANN) and genetic algorithms (GAs). In addition, the neural network is used to formulate a fitness function for predicting the value of the response based on the parameter settings. GAs then take the fitness function from the trained neural network to search for the optimal parameter combination. Owing to the most of manufactured products have more than one quality characteristic and the quality characteristics are generally correlated with each other, this study also proposes a desirability function to obtain a compromise, composite solution. A case study of how the silicon manufacturing process parameters are selected offline demonstrates the effectiveness of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
Optimal design for a ball grid array wire bonding process using a neuro-genetic approach
(2002-01-01)
Su, CT
;
Chiang, TL
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This study presents an integrated method in which neural networks, genetic algorithms, and exponential desirability functions are used to optimize the ball grid array (BGA) wire bonding process. As widely anticipated, the BGA package will become the fastest-growing semiconductor package and push integrated circuit (IC) packaging to higher level of compactness and density. However, wire bonding in BGA is difficult owing to its high input/output (I/O) count, fine pitch wire bonds, and long wire lengths. This study addresses two fundamental issues in the semiconductor assembly facility on its quest toward a defect-free manufacturing environment. First, the problem of exploring the nonlinear multivariate relationship between parameters and responses and second, obtaining the optimum operation parameters with respect to each response in which the process should operate. The implementation for the proposed method was carried out in an IC assembly factory in Taiwan; results in this study demonstrate the practicability of the proposed approach.
Optimization of TQFP molding process using neuro-fuzzy-GA approach
(2003-05-16)
Chiang, TL
;
Su, CT
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
This paper focuses on an integated optimization problem that involves multiple qualitative and quantitative responses in the thin quad flat pack (TQFP) molding process. A fuzzy quality loss function (FQLF) is first applied to the qualitative responses., since the molding defects cannot be simply represented by the relationship between molding conditions and mathematical models. Neural network is then used to provide a nonlinear relationship between process parameters and responses. A genetic algorithm together with exponential desirability function is employed to determine the optimal parameter setting for TQFP encapsulation. The proposed method was implemented in a semiconductor assembly factory in Taiwan. The results from this study have proved the feasibility of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.
Optimizing the IC wire bonding process using a neural networks/genetic algorithms approach
(2003-04-01)
Su, CT
;
Chiang, TL
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
A critical aspect of wire bonding is the quality of the bonding strength that contributes the major part of yield loss to the integrated circuit assembly process. This paper applies an integrated approach using a neural networks and genetic algorithms to optimize IC wire bonding process. We first use a back-propagation network to provide the nonlinear relationship between factors and the response based on the experimental data from a semiconductor manufacturing company in Taiwan. Then, a genetic algorithms is applied to obtain the optimal factor settings. A comparison between the proposed approach and the Taguchi method was also conducted. The results demonstrate the superiority of the proposed approach in terms of process capability.