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    National Tsing Hua University Institutional Repository > 工學院  > 工業工程與工程管理學系 > 會議論文  >  Combining Neural Networks and Genetic Algorithms for Optimizing the Parameter Design of Inter-Metal Dielectric Layer


    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46411


    Title: Combining Neural Networks and Genetic Algorithms for Optimizing the Parameter Design of Inter-Metal Dielectric Layer
    Authors: Chia-Jen Chou;Fong-Jung Yu;Chao-Ton Su
    教師: 蘇朝墩
    Date: 2008
    Publisher: Institute of Electrical and Electronics Engineers
    Relation: 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08, Dalian, 12-14 Oct. 2008, pages 1 - 4
    Keywords: circuit analysis computing
    genetic algorithms
    integrated circuit metallisation
    neural nets
    Abstract: Integrated circuits generally involve many layers of metallization as semiconductor devices require different functions; otherwise, the devices' density increases. The inter-metal dielectric (IMD) is deposited between metal layers to provide isolated capability to the device and separate the different metal layers, which are not necessary in conducting electricity. A good isolated capability will help the devices become more reliable and stable. The key problem in IMD layer is the occurrence of voids, which lead to electric leakage and cause wafer scrape. To overcome the void problem in the IMD process is difficult due to its complicated input-response relationship. In this study, the authors combined neural networks, genetic algorithms (GAs), and desirability function to optimize the IMD process. The implementation of the proposed approach was carried out in a semiconductor manufacturing company in Taiwan, and the results illustrated the practicability of the said approach.
    URI: http://www.ieee.org/
    http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/46411
    Appears in Collections:[工業工程與工程管理學系] 會議論文

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