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7. Time aggregation effect on the correlation coefficient: added-systematically sampled framework

Author

Jea, R., Su, C-T., and Lin, J-L.

Subjects
Financial research -- Models, Management science -- Methods, Business, Business, general
Abstract

The aggregation of financial and economic time series occurs in a number of ways. Temporal aggregation or systematic sampling is the commonly used approach. In this paper, we investigate the time interval effect of multiple regression models in which the variables are additive or systematically sampled. The correlation coefficient changes with the selected time interval when one is additive and the other is systematically sampled. It is shown that the squared correlation coefficient decreases monotonically as the differencing interval increases, approaching zero in the limit. When two random variables are both added or systematically sampled, the correlation coefficient is invariant with time and equal to the one-period values. We find that the partial regression and correlation coefficients between two additive or systematically sampled variables approach one-period values as n increases. When one of the variables is systematically sampled, they will approach zero in the limit. The time interval for the association analyses between variables is not selected arbitrarily or the statistical results are likely affected. Journal of the Operational Research Society (2005) 56, 1303-1309. doi:10.1057/palgrave.jors.2601947 Published online 2 March 2005 Keywords: correlation coefficient; time interval; systematic sampling

Published
2005

8. Integrated inventory model of returns-quantity discounts contract

Author

Shi, C-S and Su, C-T

Subjects
Inventories -- Accounting and auditing, Inventories -- Analysis, Management science -- Analysis, Business, Business, general
Abstract

In the traditional inventory problem, to secure demand risk a retailer often requests the right to return unsold goods, although this is associated with higher wholesale prices. Various studies have attempted to illustrate the returns scenario. However, these studies have focused on optimization from the retailer's perspective only, and have thus ignored the fact that the manufacturer might have no incentive to accept returns. This study takes account of the self-interest of both the retailer and the manufacturer, and demonstrates that a quantity discount scheme should provide the manufacturer with incentive to accept returns. A three-stage theoretical model is developed and presented to illustrate the returns-quantity discounts contract, and demonstrates that the contract is self-enforcing. Furthermore, it is demonstrated that Pareto efficiency can be attained in the model. The scenarios are illustrated through a numerical example. Journal of the Operational Research Society (2004) 55, 240-246. doi: 10.1057/palgrave.jors.2601676 Keywords: returns policy; quantity discounts; channel cooperation; Pareto efficiency

Published
2004

11. A manufacturer's optimal quantity discount strategy and return policy through game-theoretic approach

Author

Su, C-T and Shi, C-S

Subjects
Management science -- Research, Returns (Retail trade) -- Management, Company business management, Business, Business, general
Abstract

This study generalised the traditional quantity discount problem with return contracts, in which a manufacturer promises to refund some fraction of the retailer's wholesale price if an item is returned, as a two-stage game. In the first stage the manufacturer and retailer determine the inventory level cooperatively. In the second stage, the manufacturer bargains with the retailer for quantity discount and return schemes to maintain channel efficiency. A menu of discount--return combinations is proposed for the manufacturer to make inventory decisions. The model developed will demonstrate that the return policy can be considered as mirror images of quantity discount strategy. That is, options with more generous return privileges are coupled with higher wholesale prices, whereas the lowest wholesale price comes with very strict limits on returns and a restocking fee for any returned goods. Keywords: quantity discount; return policy; EOQ; inventory; two-stage game

Published
2002

19. A framework for determining MIMO process parameters by a neuro-DM&ACO approach.

Author

Wong, J.-T., Su, C.-T., and Hsieh, H.-T.

Subjects
MIMO systems, ARTIFICIAL neural networks, COMPUTER science, SEMICONDUCTOR design, MANUFACTURED products
Abstract

A framework combining artificial neural network (ANN) modelling technique, data mining and ant colony optimisation (ACO) algorithm is proposed for determining multiple-input multiple-output (MIMO) process parameters from the initial chemical-mechanical planarisation (CMP) processes used in semiconductor manufacturing. Owing to the invisibility of the ANN in the solution procedures, the decision tree approach of data mining is adopted to provide the necessary information for a real-valued ACO. The simulation result demonstrates that the proposed method can be an efficient tool for selecting properly defined parameter combination with the CMP process. [ABSTRACT FROM AUTHOR]

Published
2007
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20. Two-phased meta-heuristic methods for the post-mapping yield control problem.

Author

Su, C.-T. and Yang, C.-H.

Subjects
BUSINESS enterprises, LIQUID crystal displays, MANUFACTURING industries, MANUFACTURED products, OPERATIONS research, GENETIC algorithms, HEURISTIC, ALGORITHMS, ROBOTS, COMBINATORIAL optimization, METHODOLOGY, SIMULATED annealing, MATHEMATICS, ROBOTICS
Abstract

Yield control plays an important role in the TFT-LCD manufacturing firms, and the post-mapping operation is a crucial step. The post-mapping operation combines one TFT plate and one CF plate to form a LCD. Each TFT and CF plate is divided into a number of panels. The LCD panel is acceptable only when both TFT and CF panels are good. The TFT-LCD manufacturing firms use the sorter, a kind of robot, to increase the yield for matching TFT and CF plates. Evidently, there will be a great loss if a random mapping policy is executed. In this study, we first apply two of the most popular meta-heuristic methods to solve the post-mapping problem: Genetic Algorithm (GA) and Simulated Annealing (SA). However, when the number of matched cassettes is large, the number of ways for choosing different matched objects will become so enormous that the initial population in GA (or initial solution in SA) should be selected with a proper procedure. That is, we propose a two-phased GA and SA to improve the performance of the initial population. The basic concept of phase one is to generate an efficient initial population (or initial solution). In phase one, the initial population is created based on the optimal solution to the cassette-matching problem. In phase two, we perform GA (or SA) with the initial population created in phase one. The four different heuristic algorithms are tested for the same data to compare the various ports in the post-mapping yield control problem. The result shows that proposed two-phased algorithms provide a more excellent solution than GA and SA. [ABSTRACT FROM AUTHOR]

Published
2006
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21. Data transformation in SPC for semiconductor machinery control: A case of monitoring particles.

Author

Chen *, M.-C., Su, C.-T., Hsu, C.-C., and Liu, Y.-W.

Subjects
MANUFACTURING processes, SEMICONDUCTORS, SEMICONDUCTOR wafers, POISSON processes, MACHINERY
Abstract

Yield is an important indicator of productivity in semiconductor manufacturing. In the complex manufacturing process, the particles on wafers inevitably cause defects, which may result in chip failure and thus reduce yield. Semiconductor manufacturers initially use wafer testing to control the machine for the number of particles. This machinery control procedure aims to detect any unusual condition of machines, reduce defects in actual wafer production and thus improve yield. In practice, the distribution of particles does not usually follow a Poisson distribution, which causes an overly high rate of false alarms in applying the c -chart. Consequently, the semiconductor machinery cannot be appropriately controlled by the number of particles on machines. This paper primarily combines data transformation with the control chart based on a Neyman type-A distribution to develop a machinery control procedure applicable to semiconductor machinery. The proposed approach monitors the number of particles on the testing wafer of machines. A semiconductor company in Taiwan in the Hsinchu Science Based Industrial Park demonstrated the feasibility of the proposed method through the implementation of several machines. The implementation results indicated that the occurrence of false alarms declined extensively from 20% to 4%. [ABSTRACT FROM AUTHOR]

Published
2005
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22. The implementation of an activity-based costing collaborative planning system for semiconductor backend production.

Author

Hsu, H.-P. and Su *, C.-T.

Subjects
PRODUCTION planning, INDUSTRIAL costs, PRODUCTION engineering, PLANNING, CORPORATE finance
Abstract

While the market turns to an environment with low profit margins for semiconductor backend operations, it is hard for an independent firm to survive today. Forming strategic alliances or integrating an enterprise's internal firms by means of collaborative planning/operations to gain competitive advantage is inevitable. This paper presents the development of an activity-based costing collaborative production planning system (ABC/CPPS) to help production planners to estimate the manufacturing profit of semiconductor backend turnkey (combined IC assembly and testing) operational services at the early stage of order release to production line in a collaborative context. The estimation is under the real constraints of production resources. A predicate/transition net (Pr/Tr net) is used to simulate and implement the activity-based costing (ABC) model with the dynamic characteristics of a production line incorporated. A financial measure, profit, is used to supplement and indicate the consequence of the planning result and link the view to the enterprise's financial vision. [ABSTRACT FROM AUTHOR]

Published
2005
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23. On-line tuning of a single EWMA controller based on the neural technique.

Author

Su, C.-T. and Hsu, C.-C.

Subjects
ALGORITHMS, FLEXIBLE manufacturing systems, AUTOCORRELATION (Statistics), STATISTICAL process control, STATISTICAL correlation
Abstract

The exponentially weighted moving average (EWMA) controller has been proven to be an effective algorithm in the control the modern manufacturing system. The performance of the EWMA controlled process is based on choosing the correct EWMA gain. Most related research has focused on analysing the optimal EWMA gain in the static condition. The objective was to propose an approach based on the neural technique for on-line tuning of the single EWMA gain. The underlying approach indicated that the network learns very quickly when taking autocorrelation function and sample partial autocorrelation function patterns as the input features. It is shown that the sequence of the EWMA gains, generated by the proposed adaptive approach, converges close to the optimal controller value under several disturbance models, including IMA(1,1), and step and small ramp disturbances. In addition, the approach possesses a superior controlled output performance compared with the previous adaptive system. [ABSTRACT FROM AUTHOR]

Published
2004
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24. Intelligent scheduling controller for shop floor control systems: a hybrid genetic algorithm/decision tree learning approach.

Author

Su, C.-T. and Shiue, Y.-R.

Subjects
PRODUCTION scheduling, REAL-time control, PRODUCTION management (Manufacturing), GENETIC algorithms, DECISION trees, PROGRAMMABLE controllers
Abstract

This work develops an intelligent scheduling controller (ISC) to support a shop floor control system (SFCS) to make real-time decisions, robust to various production requirements. Selecting near-optimal subset system attributes (or features) based on various production requirements to construct ISC knowledge bases is a critical issue because of the existence of much shop floor information in an SFCS. Accordingly, this work developed a learning-based ISC methodology to acquire knowledge of a dynamic dispatching rule control mechanism. The proposed approach integrates genetic algorithms (GAs) and decision trees (DTs) learning to evolve a combinatorial optimal subset of features from possible shop floor information concerning a DT-based ISC knowledge classifier. A GA is employed to search the space of all possible subsets of a large set of candidate features. For a given feature subset, a DT algorithm is invoked to generate a DT. Applying the GA/DT-based knowledge learning mechanism to the experimental results demonstrates that the use of an optimal subset of system attributes to build scheduling knowledge bases enhanced generalization ability of the learning bias above that in the absence of an attribute selection procedure, in terms of prediction accuracy of unseen data under various performance criteria. Furthermore, simulation results indicate that the GA/DT-based ISC improves system performance in the long run over that obtained with classical DT-based ISC and the heuristic individual dispatching rule, according to various performance criteria. [ABSTRACT FROM AUTHOR]

Published
2003
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31. Poster session I * Thursday 9 December 2010, 08:30-12:30

Author

Kuznetsov, V. A., primary, Kozhurina, A. O., additional, Plusnin, A. V., additional, Szulik, M., additional, Sredniawa, B., additional, Streb, W., additional, Lenarczyk, R., additional, Stabryla-Deska, J., additional, Sedkowska, A., additional, Kowalski, O., additional, Kalarus, Z., additional, Kukulski, T., additional, Katova, T. M., additional, Nesheva, A., additional, Simova, I., additional, Hristova, K., additional, Kostova, V., additional, Boiadjiev, L., additional, Dimitrov, N., additional, Papamichalis Michalis, M. P., additional, Sitafidis George, S. G., additional, Dimopoulos Basilios, B. D., additional, Kelepesis Glafkos, G. K., additional, Economou Dimitrios, D. E., additional, Skoularigis John, J. S., additional, Triposkiadis Filippos, F. T., additional, Attenhofer Jost, C. H., additional, Pfyffer, M., additional, Naegeli, B., additional, Levis, P., additional, Faeh-Gunz, A., additional, Brunner-Larocca, H. P., additional, Velasco Del Castillo, M. S., additional, Cacicedo, A., additional, Onaindia, J. J., additional, Gonzalez Ruiz, J., additional, Subinas, A., additional, Alarcon, J. A., additional, Quintana, O., additional, Rodriguez, I., additional, Laraudogoitia, E., additional, Lam, Y.-Y., additional, Henein, M. Y., additional, Mazzone, A., additional, Vianello, A., additional, Perlini, S., additional, Corciu, A. I., additional, Cappelli, S., additional, Cerillo, A., additional, Chiappino, D., additional, Berti, S., additional, Glauber, M., additional, Herrmann, S., additional, Niemann, M., additional, Stoerk, S., additional, Strotmann, J., additional, Voelker, W., additional, Ertl, G., additional, Weidemann, F., additional, Yong, Z. Y., additional, Boerlage - Van Dijk, K., additional, Koch, K. T., additional, Vis, M. M., additional, Bouma, B. J., additional, Henriques, J. P. S., additional, Cocchieri, R., additional, De Mol, B. A. J. M., additional, Piek, J. J., additional, Baan, J., additional, Keenan, N. G. J., additional, Cueff, C., additional, Cimadevilla, C., additional, Brochet, E., additional, Lepage, L., additional, Detaint, D., additional, Iung, B., additional, Vahanian, A., additional, Messika-Zeitoun, D., additional, Otsuka, T., additional, Suzuki, M., additional, Yoshikawa, H., additional, Hashimoto, G., additional, Osaki, T., additional, Tsuchida, T., additional, Matsuyama, M., additional, Yamashita, H., additional, Ozaki, S., additional, Sugi, K., additional, Garcia Alonso, C. J., additional, Vallejo Camazon, N., additional, Ferrer Sistach, E., additional, Camara, M. L., additional, Lopez Ayerbe, J., additional, Bosch Carabante, C., additional, Espriu Simon, M., additional, Gual Capllonch, F., additional, Bayes Genis, A., additional, Deswarte, G., additional, Vanesson, C., additional, Polge, A. S., additional, Huchette, D., additional, Modine, T., additional, Marboeuf, P., additional, Lamblin, N., additional, Bauters, C., additional, Deklunder, G., additional, Le Tourneau, T., additional, Agricola, A., additional, Gullace, M., additional, Stella, S., additional, D'amato, R., additional, Slavich, M., additional, Oppizzi, M., additional, Ancona, M., additional, Margonato, A., additional, Le Ven, F., additional, Etienne, Y., additional, Jobic, Y., additional, Frachon, I., additional, Castellant, P., additional, Fatemi, M., additional, Blanc, J. J., additional, Muratori, M., additional, Montorsi, P., additional, Maffessanti, F., additional, Gripari, P., additional, Teruzzi, G., additional, Ghulam Ali, S., additional, Fusini, L., additional, Celeste, F., additional, Pepi, M., additional, Goebel, B., additional, Haugaa, K., additional, Meyer, K., additional, Otto, S., additional, Lauten, A., additional, Jung, C., additional, Edvardsen, T., additional, Figulla, H. R., additional, Poerner, T. C., additional, Aksoy, H., additional, Okutucu, S., additional, Evranos, B., additional, Aytemir, K., additional, Kaya, E. B., additional, Kabakci, G., additional, Tokgozoglu, L., additional, Ozkutlu, H., additional, Oto, A., additional, Valeur, N., additional, Pedersen, H. H., additional, Videbaek, R., additional, Hassager, C., additional, Svendsen, J. H., additional, Kober, L., additional, Tigen, M. K., additional, Karaahmet, T., additional, Gurel, E., additional, Pala, S., additional, Dundar, C., additional, Basaran, Y., additional, Caldararu, C. I., additional, Ene, E., additional, Dorobantu, M., additional, Vatasescu, R. G., additional, Cikes, M., additional, Bijnens, B., additional, Gasparovic, H., additional, Siric, F., additional, Velagic, V., additional, Lovric, D., additional, Samardzic, J., additional, Ferek-Petric, B., additional, Milicic, D., additional, Biocina, B., additional, Kjaergaard, J., additional, Ghio, S., additional, St John Sutton, M., additional, Moreau, O., additional, Kervio, G., additional, Thebault, C., additional, Leclercq, C., additional, Donal, E., additional, Mornos, C., additional, Rusinaru, D., additional, Petrescu, L., additional, Cozma, D., additional, Ionac, A., additional, Pescariu, S., additional, Dragulescu, S. I., additional, Petrovic, M. Z., additional, Vujisic-Tesic, B., additional, Milasinovic, G., additional, Petrovic, M. T., additional, Nedeljkovic, I., additional, Zamaklar-Trifunovic, D., additional, Calovic, Z., additional, Jelic, V., additional, Boricic, M., additional, Petrovic, I., additional, Kuchynka, P., additional, Palecek, T., additional, Simek, S., additional, Nemecek, E., additional, Horak, J., additional, Hulinska, D., additional, Schramlova, J., additional, Vitkova, I., additional, Aster, V., additional, Linhart, A., additional, Paluszkiewicz, L., additional, Guersoy, D., additional, Ozegowski, S., additional, Spiliopoulos, S., additional, Koerfer, R., additional, Tenderich, G., additional, Gaggl, M., additional, Heinze, G., additional, Sunder-Plassmann, G., additional, Graf, S., additional, Zehetmayer, M., additional, Voigtlaender, T., additional, Mannhalter, C., additional, Paschke, E., additional, Fauler, G., additional, Mundigler, G., additional, Tesic, M., additional, Trifunovic, D., additional, Djordjevic-Dikic, A., additional, Petrovic, O., additional, Petrovic, M., additional, Beleslin, B., additional, Ostojic, M., additional, Draganic, G., additional, Correia, C. E., additional, Rodrigues, B., additional, Santos, L. F., additional, Moreira, D., additional, Gama, P., additional, Nunes, L., additional, Nascimento, C., additional, Dionisio, O., additional, Santos, O., additional, Prinz, C., additional, Oldenburg, O., additional, Bitter, T., additional, Piper, C., additional, Horstkotte, D., additional, Faber, L., additional, Nemes, A., additional, Gavaller, H., additional, Csanady, M., additional, Forster, T., additional, Calcagnino, M., additional, O'mahony, C., additional, Tsovolas, K., additional, Lambiase, P. D., additional, Elliott, P., additional, Olezac, A. S., additional, Bensaid, A., additional, Nahum, J., additional, Teiger, E., additional, Dubois-Rande, J. L., additional, Gueret, P., additional, Lim, P., additional, Langer, C., additional, Kansal, M., additional, Surapaneni, P., additional, Sengupta, P. P., additional, Lester, S. J., additional, Ommen, S. R., additional, Ressler, S. W., additional, Hurst, R. T., additional, Monivas Palomero, V., additional, Mingo Santos, S., additional, Mitroi, C., additional, Garcia Lunar, I., additional, Garcia Pavia, P., additional, Gonzalez Mirelis, J., additional, Ruiz Bautista, L., additional, Castro Urda, V., additional, Toquero Ramos, J., additional, Fernandez Lozano, I., additional, Sommer, A., additional, Poulsen, S. H., additional, Mogensen, J., additional, Thuesen, L., additional, Egeblad, H., additional, Montisci, R., additional, Ruscazio, M., additional, Vacca, A., additional, Garau, P., additional, Tuveri, F., additional, Soro, C., additional, Matthieu, A., additional, Meloni, L., additional, Kosmala, W., additional, Przewlocka-Kosmala, M., additional, Wojnalowicz, A., additional, Mysiak, A., additional, Marwick, T. H., additional, Yotti, R., additional, Ripoll, C., additional, Bermejo, J., additional, Benito, Y., additional, Mombiela, T., additional, Rincon, D., additional, Barrio, A., additional, Banares, R., additional, Fernandez-Aviles, F., additional, Tomaszewski, A., additional, Kutarski, A., additional, Tomaszewski, M., additional, Ticulescu, R., additional, Vriz, O., additional, Sparacino, L., additional, Popescu, B. A., additional, Ginghina, C., additional, Nicolosi, G. L., additional, Carerj, S., additional, Antonini-Canterin, F., additional, Agricola, E., additional, Bertoglio, L., additional, Melissano, G., additional, Chiesa, R., additional, Garcia Blas, S., additional, Iglesias Del Valle, D., additional, Lopez Fernandez, T., additional, Gomez De Diego, J. J., additional, Monedero Martin, M. C., additional, Dominguez, F. J., additional, Moreno Yanguela, M., additional, Lopez Sendon, J. L., additional, Adhya, S., additional, Murgatroyd, F. D., additional, Monaghan, M., additional, Spinarova, L., additional, Meluzin, J., additional, Hude, P., additional, Krejci, J., additional, Podrouzkova, H., additional, Pesl, M., additional, Panovsky, R., additional, Dusek, L., additional, Orban, M., additional, Korinek, J., additional, Hammerstingl, C., additional, Schwiekendik, M., additional, Nickenig, G., additional, Momcilovic, D., additional, Lickfett, L., additional, Beladan, C. C., additional, Calin, A., additional, Rosca, M., additional, Muraru, D., additional, Voinea, F., additional, Popa, E., additional, Matei, F., additional, Curea, F., additional, Di Salvo, G., additional, Pacileo, G., additional, Gala, S., additional, Castaldi, B., additional, D'aiello, A. F., additional, Mormile, A., additional, Baldini, L., additional, Russo, M. G., additional, Calabro, R., additional, Halvorsen, P. S., additional, Dahle, G., additional, Bugge, J. F., additional, Bendz, B., additional, Aaberge, L., additional, Rein, K. A., additional, Fiane, A., additional, Bergsland, J., additional, Fosse, E., additional, Aakhus, S., additional, Koopman, L. P., additional, Chahal, N., additional, Slorach, C., additional, Hui, W., additional, Sarkola, T., additional, Manlhiot, C., additional, Bradley, T. J., additional, Jaeggi, E. T., additional, Mccrindle, B. W., additional, Mertens, L., additional, D'aiello, F. A., additional, Mormilw, A., additional, Rea, A., additional, O'Connor, K., additional, Romano, G., additional, Magne, J., additional, Pierard, L., additional, Lancellotti, P., additional, Arita, T., additional, Ando, K., additional, Isotani, A., additional, Soga, Y., additional, Iwabuchi, M., additional, Nobuyoshi, M., additional, Wiesen, M., additional, Skowasch, D., additional, Breunig, F., additional, Beer, M., additional, Hu, K., additional, Wanner, C., additional, Morel, M. A., additional, Bernard, Y. F., additional, Descotes-Genon, V., additional, Meneveau, N., additional, Schiele, F., additional, Vitarelli, A., additional, Bernardi, M., additional, Scarno, A., additional, Caranci, F., additional, Padella, V., additional, Dettori, O., additional, Capotosto, L., additional, Vitarelli, M., additional, De Cicco, V., additional, Bruno, P., additional, Bajraktari, G., additional, Lindqvist, P., additional, Gustafsson, U., additional, Holmgren, A., additional, Hassan, M., additional, Said, K., additional, Baligh, E., additional, Farouk, H., additional, Osama, D., additional, Elmahdy, M. F., additional, Elfaramawy, A., additional, Sorour, K., additional, Luckie, M., additional, Zaidi, A., additional, Fitzpatrick, A., additional, Khattar, R. S., additional, Schwartz, J., additional, Huttin, O., additional, Popovic, B., additional, Zinzius, P. Y., additional, Christophe, C., additional, Marcon, O., additional, Groben, L., additional, Juilliere, Y., additional, Chabot, F., additional, Selton-Suty, C., additional, Krastev, B., additional, Kinova, E. T. K., additional, Zlatareva, N. I. Z., additional, Goudev, A. R. G., additional, Teske, A. J., additional, De Boeck, B. W., additional, Mohames Hoesein, F. A., additional, Van Driel, V., additional, Loh, P., additional, Cramer, M. J., additional, Doevendans, P. A., additional, Dillenburg, F., additional, Abd El Salam, K. M., additional, Ho, E. M. M., additional, Hall, M., additional, Hemeryck, L., additional, Bennett, K., additional, Scott, K., additional, King, G., additional, Murphy, R. T., additional, Mahmud, A., additional, Brown, A. S., additional, Dalen, H., additional, Thorstensen, A., additional, Romundstad, P. R., additional, Aase, S. A., additional, Stoylen, A., additional, Vatten, L., additional, Bochenek, T., additional, Wita, K., additional, Tabor, Z., additional, Doruchowska, A., additional, Lelek, M., additional, Trusz-Gluza, M., additional, Hamodraka, E., additional, Paraskevaidis, I., additional, Karamanou, A., additional, Michalakeas, C., additional, Vrettou, H., additional, Kapsali, E., additional, Tsiapras, D., additional, Lekakis, I., additional, Anastasiou-Nana, M., additional, Kremastinos, D., additional, Sirugo, L., additional, Bottari, V. E., additional, Licciardi, S., additional, Blundo, A., additional, Atanasio, A., additional, Monte, I. P., additional, Park, C. S., additional, Kim, J. H., additional, Cho, J. S., additional, Kim, M. J., additional, Cho, E. J., additional, Ihm, S. H., additional, Jung, H. O., additional, Jeon, H. K., additional, Youn, H. J., additional, Kim, K. S., additional, Fontana, A., additional, Taravella, L., additional, Zambon, A., additional, Trocino, G., additional, Giannattasio, C., additional, Kalinin, A., additional, Alekhin, M., additional, Bahs, G., additional, Lejnieks, A., additional, Kalvelis, A., additional, Kalnins, A., additional, Shipachovs, P., additional, Zakharova, E., additional, Blumentale, G., additional, Trukshina, M., additional, Biering-Sorensen, T., additional, Mogelvang, R., additional, Haahr-Pedersen, S., additional, Schnohr, P., additional, Sogaard, P., additional, Skov Jensen, J., additional, Gargani, L., additional, Agoston, G., additional, Capati, E., additional, Badano, L., additional, Moreo, A., additional, Costantino, M. F., additional, Caputo, M. L., additional, Mondillo, S., additional, Sicari, R., additional, Picano, E., additional, Malev, E. G., additional, Timofeev, E. V., additional, Reeva, S. V., additional, Zemtsovsky, E. V., additional, Piazza, R., additional, Enache, R., additional, Roman-Pognuz, A., additional, Leiballi, E., additional, Pecoraro, R., additional, Sadeghian, H., additional, Lotfi_Tokaldany, M., additional, Rezvanfard, M., additional, Kasemisaeid, A., additional, Majidi, S., additional, Montazeri, M., additional, Saber-Ayad, M., additional, Nassar, Y. S., additional, Farhan, A., additional, Moussa, A., additional, El-Sherif, A., additional, Cooper, R. M., additional, Somauroo, J. D., additional, Shave, R. E., additional, Williams, K. L., additional, Forster, J., additional, George, C., additional, Bett, T., additional, Gaze, D. C., additional, George, K. P., additional, Mansencal, N., additional, Dupland, A., additional, Caille, V., additional, Perrot, S., additional, Bouferrache, K., additional, Vieillard-Baron, A., additional, Jouffroy, R., additional, Cioroiu, S. G., additional, Alexe, O. S., additional, Bobescu, E., additional, Rus, H., additional, Schiano Lomoriello, V., additional, Esposito, R., additional, Santoro, A., additional, Raia, R., additional, Farina, F., additional, Ippolito, R., additional, Galderisi, M., additional, Aburawi, E. H., additional, Malcus, P., additional, Thuring, A., additional, Maxedius, A., additional, Pesonen, E., additional, Nair, S. V., additional, Joyce, E., additional, Lee, L., additional, Shrimpton, J., additional, Newman, E., additional, James, P. R., additional, Jurcut, C., additional, Caraiola, S., additional, Jurcut, R. O., additional, Giusca, S., additional, Nitescu, D., additional, Amzulescu, M. S., additional, Copaci, I., additional, Tanasescu, C., additional, Silva Marques, J., additional, Silva, D., additional, Ferreira, F., additional, Ferreira, P. C., additional, Almeida, A. G., additional, Martim Martins, J., additional, Lopes, M. 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Published
2010
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45. An Experimental Approach to Enhance Cu Wire Bonding Yield Through Parameter Optimization.

Author

Su, C.-T., Yeh, C.-J., Chou, C.-J., and Chang, C. Alec

Subjects
*GOLD wire, *WIRE bonding (Electronic packaging), *SEMICONDUCTOR industry, *COPPER wire, *GENETIC algorithms
Abstract

Gold (Au) wire is the preferred material for wire bonding in the semiconductor industry. However, the rising price of Au has become a key issue in IC assembly and design. To stay competitive, costs must be reduced. Copper (Cu) wire can also be used in wire bonding. Cu wire is cheaper than Au wire. To obtain the best yield of wire bonding and to reduce cost, this study develops an experimental approach by utilizing neural networks to establish the functional relationship between control factors and responses and then applying genetic algorithms to obtain the optimal control factor settings of the Cu wire bonding process. Using the developed approach for Cu wire bonding parameter design, the production yield increased from 98.5 to 99.65%, resulting in approximately USD 1.16 million in savings. [ABSTRACT FROM AUTHOR]

Published
2014
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