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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/74675

    Title: 灰色理論與類神經網路在晶片溫度分佈之研究
    Other Titles: Study of grey theory and artificial neural network applied in the temperature distribution of IC chip
    Authors: 陳尚彥;Chen, Shang-Yan
    Contributors: 淡江大學機械與機電工程學系博士班
    楊智旭;Yang, Jr-Syu
    Keywords: 灰色理論;類神經網路;熱設計;電子散熱;grey theory;Neural Networks;Thermal design;Electronic Cooling
    Date: 2011
    Issue Date: 2011-12-28 19:16:43 (UTC+8)
    Abstract: 本論文有兩個主要研究目標,一是發展出人工神經網路進行多熱源晶片封裝之溫度分佈研究,另一目標是應用灰色理論在多熱源晶片封裝溫度之最高溫度、平均溫度的預測。
    研究結果得知,比較倒傳遞神經網路(Back-Propagation Neural Network, BPN)與Icepak套裝軟體兩種方法顯示,BPN準確度達97﹪,由此顯示應用BPN在多熱源晶片封裝之溫度分佈預測上,BPN是可行、可用的。
    An objective of this thesis is to develop an ANN model to obtain the temperature distribution of a IC chip in the packaging processes. Another objective is to predict the highest temperature and mean temperature of it by using the Grey system theory. The calculated results of BPN(Back-Propagation Neural Network) are compared with the simulated results of the software package(Icepack). The accuracy of the results between these two methods is 97%. It shows that the BPN technique is useful to predict the temperature distribution of the IC packaging.
    The advantage of the Grey prediction model is the higher calculating efficiency for a few original data (at least 4 data in the series). In the research, the highest and mean temperatures of eight chips from Icepack simulation are set to be the original data in the GM(1,1) model separately. Then, the predicted results are applied to make the post residual error analysis. The analyzed results show that this Grey prediction model is belong to the “Good” grade. It means this developed GM(1,1) model is precise and accurate.
    The contribution of this research results can be applied to the thermal analysis and design for an IC chip packaging processes.
    Appears in Collections:[機械與機電工程學系暨研究所] 學位論文

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