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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 盧信嘉 | |
dc.contributor.author | Chia-Ming Liu | en |
dc.contributor.author | 劉嘉銘 | zh_TW |
dc.date.accessioned | 2021-06-15T02:30:51Z | - |
dc.date.available | 2009-08-18 | |
dc.date.copyright | 2009-08-18 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-08-14 | |
dc.identifier.citation | [1]Shiuan-Ming Su, Sung-Mao Wu, Chi-Chang Lai, Yu-Che Tai, Wang-Yu Lin and Sheng-Wei Guan, “Analysis and modeling of IPD for spiral inductor on glass substrate,” in 2008 International Conference on Microwave and Millimeter Wave Technology, vol.3, pp.1274-1277, April 2008.
[2]張斐章、張麗秋,”類神經網路”,東華書局,民國94年九月。 [3]Dan W. Patterson, “Artificial Neural Networks:Theory and Application,” Prentice Hall, 1996. [4]H. A. Wheeler, “Formulas for the skin effect,” Proceedings of the Institute of Radio Engineers, Vol. 30, pp. 412-424, Sept. 1942, [5]J. A. Tegopoulos and E. E. Kriezis, “Eddy currents in linear conducting media,” Studies in Electrical and Electronic Engineering, vol. 16, Amsterdam: Elsevier, 1985. [6]C.P. Yue and S.S.Wong, “Physical modeling of spiral inductors on silicon,” IEEE Transactions on Electron Devices, Volume: 47 Issue: 3, pp. 560-568, March. 2000. [7]Richard K. Ulrich and Leonard W. Schaper editors, Integrated Passive Component Technology, Wiley & Sons Inc., 2003. [8]K. B. Ashby, I. A. Koullias, W. C. Finley, J. J. Bastek, and S. Moinian, “High Q inductors for wireless applications in a complementary silicon bipolar process,” IEEE J. Solid-State Circuits, vol. 31, pp. 4-9, Jan. 1996. [9]C.P. Yue, “A physical model for planar spiral inductors on silicon,” IEEE Electron Devices Letters, vol. 43, pp.155-158, Dec. 1996. [10]Sheng-Mou Lin, Li-Qun Yang, and Hong-Yang Chang, “Scalable lumped model with multiple physical parameters for embedded passives,” in Proc. IEEE Electronic Components and Technology Conference, vol. 2, May 2005, pp. 1842-1845. [11]A. E. Ruehli and H. Heeb., “Circuit models for three-dimensional geometries including dielectrics,” IEEE Transactions on Microwave Theory and Techniques, vol. 40, pp.1507-1516, July 1992. [12]A. E. Ruehli, U. Miekkala, and H. Heeb., “Stability of discretized partial element equivalent efie circuit models,” IEEE Transactions on Antennas and Propagation, vol. 43, issue 6, pp. 553-559, June 1995. [13]A.E. Ruehli., “Equivalent circuit models for three-dimensional multiconductor systems,” IEEE Transactions on Microwave Theory and Techniques, vol. 22, issue 3, pp. 216-221, March 1974. [14]Tuck Boon Chan, Hsin-Chia Lu, Jun-Kuei Zeng and Charlie Chung-Ping Chen, “LTCC spiral inductor modeling, synthesis, and optimization,” 12th Asia and South Pacific Design Automation Conference ASP-DAC 2008, Jan. 2008, pp.768-781. [15]Q.J. Zhang, ”Electromagnetic modeling and optimization using neural networks,” 5th. European Congress on Computational Methods in Applied Sciences and Engineering, June 30, 2008, pp.1255-1258. [16]Rana J. Pratap, Saikat Sarkar, Stephane Pinel, Joy Laskar, and Gary S. May, “Modeling and optimization of multilayer LTCC inductors for RF/wireless applications using neural network and genetic algorithms,” 2004 IEEE Electronic Components and Technology Conference, vol. 1, June 2004, pp.248-254. [17]Agilent Advanced Design System (ADS)/Momentum RF v.2008: EDA tools, Agilent Technologies, Inc. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43861 | - |
dc.description.abstract | 本篇論文提出利用倒傳遞類神經網路自動合成出使用者需要的微波頻電感。由於RF被動電路設計過程中,電感在眾多被動元件中影響整體效能最多的,而電感的設計往往也是最耗時最難處理的部份。
為了解決以上的問題,我們利用類神經網路提出了一套自動化合成電感的方法,由於類神經網路具有良好的預測能力,能夠由電感尺寸精確估算電感值及品質因素。根據模擬或量測的資料,作為倒傳遞類神經網路學習的依據,可以訓練出精確且計算快速的類神經模型,利用這些類神經模型可以加快電感設計的時程,依使用者的需求自動合成出矩形螺旋電感,同時保有電磁模擬軟體的準確性。 | zh_TW |
dc.description.abstract | This thesis brings forward an automatic inductor synthesis tool at microwave band according to the user’s specification by using back-propagation neural networks (BPNNs). Inductor is one of critical component that dominate the overall performance of RF devices in RF design, and inductor design is often one of most difficult and time consuming process.
In order to solve the above problem, we propose the method of automatic inductor synthesis using neural networks. The results of EM simulation or measurement of inductors served as a basis for neural network training with suitable training procedure. The neural networks then can predict inductance and Q-value for a given inductor layout. These neural models can speed up the inductor design by automatically generating the layout of planar spiral inductors according to the user’s specification. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T02:30:51Z (GMT). No. of bitstreams: 1 ntu-98-R96943113-1.pdf: 2929499 bytes, checksum: 934766b8240d86c9319358843962ab7c (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 第 1 章 簡介 1
1.1 動機 1 1.2 整合被動元件(Integrated passive device)製程 2 1.2.1 Low-K 物質的優點 2 1.2.2 Low-K 物質的缺點 3 1.2.3 如何利用IPD製程製作被動元件 3 1.3 論文貢獻 6 1.4 章節介紹 7 第 2 章 理論概述 8 2.1 何謂類神經網路 8 2.2 類神經網路架構 9 2.3 倒傳遞類神經網路 12 2.3.1 倒傳遞網路基本架構及特性 13 2.3.2 誤差倒傳遞演算法(EBP) 14 2.3.3 類神經網路的重要參數 19 2.4 電感模型及電氣特性 22 2.4.1 典型螺旋型電感 22 2.4.2 螺旋型電感等效電路 23 2.4.3 元件參數的萃取 24 2.4.4 品質因素 26 2.5 前人相關研究 26 第 3 章 研究架構與模式建立 28 3.1 研究架構 28 3.2 電感模擬與參數萃取 29 3.3 模式建立 30 3.4 搜尋最佳結構電感 37 第 4 章 結果與討論 39 4.1 模擬數據與類神經網路輸出比對 39 4.2 方形螺旋電感比較 41 4.3 矩形螺旋電感比較 54 4.4 訓練樣本數與精確度比較 65 第 5 章 結論 68 參考文獻 69 附錄A 71 | |
dc.language.iso | zh-TW | |
dc.title | 使用倒傳遞類神經網路自動合成IPD製程上之微波頻電感 | zh_TW |
dc.title | Automatic inductor synthesis at microwave band under integrated passive device process using BPNN | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳中平,李建模,江介宏 | |
dc.subject.keyword | 倒傳遞類神經網路,整合被動元件,電感,合成, | zh_TW |
dc.subject.keyword | BPNN,IPD,inductor,synthesis, | en |
dc.relation.page | 72 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-08-17 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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