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Technology Evaluation and Selection of 3DIC Integration Using a Three-Stage Fuzzy MCDM

Author

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  • Yen-Chun Lee

    (Office of Research and Development, National Chiao Tung University, 1001, Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

  • C. James Chou

    (Institute of Management of Technology, National Chiao Tung University, 1001, Ta-Hsueh Rd., Hsinchu 30010, Taiwan)

Abstract

For the purpose of the sustainable development in the global semiconductor industry, emerging three-dimensional integrated circuit (3DIC) integration technologies have demonstrated their importance as potential candidates for extending the lifespan of Moore’s Law. This study aimed to explore a technology selection process involving a three-stage fuzzy multicriteria decision-making (MCDM) approach to facilitate the effective assessment of emerging 3DIC integration technologies. The fuzzy Delphi method was first used to determine the important criteria. The fuzzy analytic hierarchy process (fuzzy AHP) was then adopted to derive the weights of the criteria. The fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS) was finally deployed to rate the alternatives. Empirical results indicate that market potential, time-to-market, and heterogeneous integration are the top three decision criteria for the selection of 3DIC integration technologies. Furthermore, 2.5D through-silicon interposer (TSI) is of primary interest to the Taiwanese semiconductor industry, followed by 3DIC through-silicon via (TSV), 3D packaging, and 3D silicon TSV (Si TSV). The proposed three-stage fuzzy decision model may potentially assist industry practitioners and government policy-makers in directing research and development investments and allocating resources more strategically.

Suggested Citation

  • Yen-Chun Lee & C. James Chou, 2016. "Technology Evaluation and Selection of 3DIC Integration Using a Three-Stage Fuzzy MCDM," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:2:p:114-:d:62915
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    References listed on IDEAS

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    1. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    2. Awasthi, Anjali & Chauhan, Satyaveer S. & Goyal, S.K., 2010. "A fuzzy multicriteria approach for evaluating environmental performance of suppliers," International Journal of Production Economics, Elsevier, vol. 126(2), pages 370-378, August.
    3. van de Kaa, Geerten & Rezaei, Jafar & Kamp, Linda & de Winter, Allard, 2014. "Photovoltaic technology selection: A fuzzy MCDM approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 662-670.
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    Cited by:

    1. Min-Sung Kim & Eul-Bum Lee & In-Hye Jung & Douglas Alleman, 2018. "Risk Assessment and Mitigation Model for Overseas Steel-Plant Project Investment with Analytic Hierarchy Process—Fuzzy Inference System," Sustainability, MDPI, vol. 10(12), pages 1-17, December.
    2. Katarzyna Halicka, 2020. "Technology Selection Using the TOPSIS Method," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 14(1), pages 85-96.
    3. Keun-Sik Park & Young-Joon Seo & A-Rom Kim & Min-Ho Ha, 2018. "Ship Acquisition of Shipping Companies by Sale & Purchase Activities for Sustainable Growth: Exploratory Fuzzy-AHP Application," Sustainability, MDPI, vol. 10(6), pages 1-13, May.
    4. Zefang Zhao & Yanlong Guo & Haiyan Wei & Qiao Ran & Wei Gu, 2017. "Predictions of the Potential Geographical Distribution and Quality of a Gynostemma pentaphyllum Base on the Fuzzy Matter Element Model in China," Sustainability, MDPI, vol. 9(7), pages 1-15, July.
    5. Sonntag, Dominik, 2018. "Die Theorie der fairen geometrischen Rendite [The Theory of Fair Geometric Returns]," MPRA Paper 87082, University Library of Munich, Germany.

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