許多可攜式或是植入式的微系統裝置依據其不同的生醫領域應用，會和感測器陣列結合，而這些感測器的反應通常是高維度且充滿雜訊或是飄移的，為了要讓植入式裝置能直接於生物體內進行診斷或是減少無線傳輸的資料量，會需要一個低功耗且能即時做訊號前處理的內嵌式裝置。在各種訊號處理演算法中，機率模型能夠使用其運算的隨機性來概括資料本身的變異，有助於系統進行穩健的訊號辨識，而連續侷限型波茲曼演算法，使用隨機型類神經網路的架構，已經於先前文獻中展現了優異且穩健的生醫訊號處理能力，並且能夠透過其學習能力即時調整參數維持良好的分群降維效果，非常適合扮演系統中訊號前處理的腳色。
本論文主要探討將連續侷限型波茲曼演算法應用於電子鼻系統作為訊號前處理裝置的方法，首先以軟體進行模擬驗證連續侷限型波茲曼演算法處理電子鼻感測訊號的可行性，接著進一步研究將演算法實現成硬體的方法，而演算法硬體已經成功以TSMC 0.18μm與90nm製程實現，並整合入電子鼻系統當中，使用LabVIEW軟體以及資料擷取卡系統設計實驗平台，以氣體資料進行量測的結果也證實了演算法硬體能夠有符合預期的訊號處理效果。

Many portable or implantable microsystems have incorporated sensor arrays for various biomedical applications. The raw sensory signals are usually high-dimensional, noisy, and drifting. To facilitate in-situ diagnosis or to reduce the data for wireless transmission, a low-power, embedded system is demanded for fusing the sensory signals robustly in real time. A probabilistic neural network called the Continuous Restricted Boltzmann Machine (CRBM) has been shown capable of classifying biomedical data reliably. Thus, it is suitable for CRBM to act as a signal pre-processing unit in system.
This paper discuss about how to use CRBM to process sensory data of electronic nose system. At first, it makes pilot simulation in software to confirm the capability of CRBM for processing sensory data. Then it will study the method of implementing CRBM into VLSI (Very Large Scale Integration) and integrating with electronic nose system. The chip of CRBM integrating with electronic nose system has been designed and fabricated with the TSMC 0.18μm and 90nm technology provided by TSMC (Taiwan Semiconductor Manufacturing Company). The measurement results proved that the CRBM hardware system can perform good processed results as expected.

誌謝 I
摘要 III
Abstract V
目錄 VII
圖目錄 IX
表目錄 XIV
第一章 緒論 1
1.1 研究動機 1
1.2 論文貢獻 2
1.3 論文內容概述 3
第二章 文獻回顧 5
2.1 電子鼻感測器簡介 5
2.2 連續侷限型波茲曼演算法(CRBM)簡介 7
2.3 連續侷限型波茲曼演算法應用於電子鼻資料處理 11
2.4 連續侷限型波茲曼演算法於積體電路之實現 12
2.5 總結 13
第三章 連續侷限型波茲曼演算法應用於電子鼻氣體實驗 15
3.1 電子鼻氣體實驗結果 15
3.1.1 水果氣體實驗 17
3.1.2 假酒氣體實驗 20
3.1.3 肺炎病菌氣體實驗 24
3.2 生醫訊號實驗結果 28
3.2.1 神經訊號實驗 28
3.2.2 心電訊號實驗 32
3.3 總結 34
第四章 連續侷限型波茲曼演算法之積體電路設計 35
4.1 混合訊號連續侷限型波茲曼演算法(CRBM)電路介紹 35
4.1.1 混合訊號CRBM電路架構與時序 35
4.1.2 混合訊號CRBM核心電路 38
4.1.3 混合訊號CRBM效益評估 45
4.2 數位連續侷限型波茲曼演算法(CRBM)電路設計 47
4.2.1 數位 CRBM 系統架構與操作時序 47
4.2.2 數位 CRBM 系統子電路設計 51
4.2.3 0.18μm 1V數位CRBM 系統設計 57
4.2.4 90nm 0.5V數位CRBM 系統設計 60
4.3 數位連續侷限型波茲曼演算法與電子鼻系統晶片整合 61
4.4 電路模擬結果與晶片規格 65
4.5 總結 69
第五章 晶片量測結果 71
5.1 量測方法與平台設置 71
5.2 LabVIEW與資料擷取卡量測平台設置 71
5.3 量測結果 73
5.4 總結 85
第六章 結論及未來研究方向 87
6.1 結論 87
6.2 未來研究方向 88
參考文獻 89

[1] Kea-Tiong Tang, Shih-Wen Chiu, Meng-Fan Chang, Chih-Cheng Hsieh, and Jyuo-Min Shyu, “A low-power electronic nose signal-processing chip for a portable artificial olfaction system.,” IEEE transactions on biomedical circuits and systems, vol. 5, no. 4, pp. 380–90, Aug. 2011.
[2] Hung Tat Chen, Kwan Ting Ng, a Bermak, M. K. Law, and D. Martinez, “Spike latency coding in biologically inspired microelectronic nose.,” IEEE transactions on biomedical circuits and systems, vol. 5, no. 2, pp. 160–8, Apr. 2011.
[3] T. J. Koickal, A. Hamilton, S. L. Tan, J. A. Covington, J. W. Gardner, and T. C. Pearce, “Analog VLSI Circuit Implementation of an Adaptive Neuromorphic Olfaction Chip,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 54, no. 1, pp. 60–73, Jan. 2007.
[4] H. Chen and A. F. Murray, “Continuous restricted Boltzmann machine with an implementable training algorithm,” IEE Proceedings - Vision, Image, and Signal Processing, vol. 150, no. 3, p. 153, 2003.
[5] C. C. Lu, C. Y. Hong, and H. Chen, “A Scalable and Programmable Architecture for the Continuous Restricted Boltzmann Machine in VLSI,” 2007 IEEE International Symposium on Circuits and Systems, pp. 1297–1300, May 2007.
[6] N. H. Hamid, A. F. Murray, and S. Roy, “Time-Domain Modeling of Low-Frequency Noise in Deep-Submicrometer MOSFET,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 55, no. 1, pp. 245–257, Feb. 2008.
[7] N. Friedman, M. Linial, I. Nachman, and D. Pe’er, “Using Bayesian networks to analyze expression data.,” Journal of computational biology : a journal of computational molecular cell biology, vol. 7, no. 3–4, pp. 601–20, Jan. 2000.
[8] K. Strimmer and V. Moulton, “Likelihood analysis of phylogenetic networks using directed graphical models.,” Molecular biology and evolution, vol. 17, no. 6, pp. 875–81, Jun. 2000.
[9] C. Lu and H. Chen, “Minimising Contrastive Divergence with Dynamic Current Mirrors,” ICANN ’09 Proceedings of the 19th International Conference on Artificial Neural Networks, pp. 410–420, 2009.
[10] R. A. Fisher, “The Use of Multiple Measurements In Taxonomic Problems,” Annals of Eugenics, vol. 7, no. 2, pp. 179–188, Sep. 1936.
[11] C. C. Lu and H. Chen, “A Scalable and Programmable Probabilistic Generative Model in Very Large Scale Integration,” pp. 1–10, 2010.
[12] A. Annema, B. Nauta, R. van Langevelde, and H. Tuinhout, “Analog circuits in ultra-deep-submicron CMOS,” IEEE Journal of Solid-State Circuits, vol. 40, no. 1, pp. 132–143, Jan. 2005.
[13] T. D. and R. D. Shih-Chii Liu, Jörg Kramer, Giacomo Indiveri, Analog VLSI: Circuits and Principles. The MIT Press, 2002.
[14] M. Banu and Y. Tsividis, “Floating voltage-controlled resistors in CMOS technology,” Electronics Letters, vol. 18, no. 15, p. 678, 1982.
[15] C. Lin and J. Wang, “A digital circuit design of hyperbolic tangent sigmoid function for neural networks,” 2008 IEEE International Symposium on Circuits and Systems, pp. 856–859, May 2008.
[16] A. Mishra and K. Raj, “Implementation of a digital neuron with nonlinear activation function using piecewise linear approximation technique,” in 2007 Internatonal Conference on Microelectronics, 2007, no. December, pp. 69–72.
[17] P. Treleaven, M. Pacheco, and M. Vellasco, “VLSI architectures for neural networks,” IEEE Micro, vol. 9, no. 6, pp. 8–27, Dec. 1989.
[18] S. Vassiliadis, M. Zhang, and J. G. Delgado-Frias, “Elementary function generators for neural-network emulators.,” IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, vol. 11, no. 6, pp. 1438–49, Jan. 2000.
[19] Z. Xu, J. Chen, L. Xu, W. Wang, and A. E. Methods, “A parallel digital hardware generator of White Gaussian noise stressing on crest factor,” in 2012 18th Asia-Pacific Conference on Communications (APCC), 2012, pp. 456–461.
[20] W. Hörmann and J. Leydold, “Continuous random variate generation by fast numerical inversion,” ACM Transactions on Modeling and Computer Simulation, vol. 13, no. 4, pp. 347–362, Oct. 2003.
[21] G. E. P. Box and M. E. Muller, “A Note on the Generation of Random Normal Deviates,” The Annals of Mathematical Statistics, vol. 29, no. 2, pp. 610–611, Jun. 1958.
[22] W. Luk, J. D. Villasenor, and P. H. W. Leong, “A hardware Gaussian noise generator using the Wallace method,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 13, no. 8, pp. 911–920, Aug. 2005.
[23] L. Colavito and D. Silage, “Composite Look-Up Table Gaussian Pseudo-Random Number Generator,” 2009 International Conference on Reconfigurable Computing and FPGAs, pp. 314–319, Dec. 2009.