{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T08:06:00Z","timestamp":1768809960580,"version":"3.49.0"},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2008,2]]},"abstract":"<jats:p> Yield forecasting is a very important task to a semiconductor manufacturing factory. To enhance both the precision and accuracy of semiconductor yield forecasting, a fuzzy-neural system incorporating unequally important expert opinions is constructed in this study. In the proposed methodology, multiple experts construct their own fuzzy yield learning models from various viewpoints to predict the yield of a product. Besides, these expert opinions can also be considered unequally important. To aggregate these fuzzy yield forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to aggregate the fuzzy yield forecasts into a polygon-shaped fuzzy yield forecast, in order to improve the precision of yield forecasting. After that, a back propagation network is constructed to defuzzify the polygon-shaped fuzzy yield forecast and to generate a representative\/crisp value, so as to enhance the accuracy. For evaluating the effectiveness of the proposed methodology and to make comparison with some existing approaches, all approaches were applied to the practical data of three products in a real semiconductor manufacturing factory. According to experimental results, the proposed methodology improved both the precision and accuracy of semiconductor yield forecasting by 48% and 38%, respectively. <\/jats:p>","DOI":"10.1142\/s0218488508005030","type":"journal-article","created":{"date-parts":[[2008,2,12]],"date-time":"2008-02-12T05:09:10Z","timestamp":1202792950000},"page":"35-58","source":"Crossref","is-referenced-by-count":92,"title":["A FUZZY-NEURAL SYSTEM INCORPORATING UNEQUALLY IMPORTANT EXPERT OPINIONS FOR SEMICONDUCTOR YIELD FORECASTING"],"prefix":"10.1142","volume":"16","author":[{"given":"TOLY","family":"CHEN","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung City 407, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YU-CHENG","family":"LIN","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, The Overseas Chinese Institute of Technology, Taichung City 407, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-004-2382-2"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2003.815636"},{"key":"rf3","first-page":"355","volume":"17","author":"Li T.-S.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-004-2067-x"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-005-0240-5"},{"key":"rf6","volume-title":"Learning and Strategic Product Innovation: Theory and Evidence for the Semiconductor Industry","author":"Gruber H.","year":"1984"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.2307\/2555574"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.2307\/3003508"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.42.5.680"},{"key":"rf10","volume-title":"Applications of Fuzzy Set Theory in Human Factors","author":"Watada J.","year":"1986"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1109\/66.762883"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-004-2382-2"},{"key":"rf13","author":"Dwivedi S.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.2307\/3003508"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90144-9"},{"key":"rf16","author":"Kwong C. 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S.","journal-title":"Fuzzy Sets and Systems"},{"key":"rf18","volume-title":"Fuzzy Mathematical Models in Engineering and Management Science","author":"Kaufmann A.","year":"1998"},{"key":"rf19","author":"Chen T.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"rf20","doi-asserted-by":"publisher","DOI":"10.1016\/j.mcm.2006.04.014"}],"container-title":["International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218488508005030","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:25:35Z","timestamp":1565184335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218488508005030"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,2]]},"references-count":20,"journal-issue":{"issue":"01","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,2]]}},"alternative-id":["10.1142\/S0218488508005030"],"URL":"https:\/\/doi.org\/10.1142\/s0218488508005030","relation":{},"ISSN":["0218-4885","1793-6411"],"issn-type":[{"value":"0218-4885","type":"print"},{"value":"1793-6411","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,2]]}}}