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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 林致廷 | |
| dc.contributor.author | Wei-En Hsu | en |
| dc.contributor.author | 徐偉恩 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:06:56Z | - |
| dc.date.available | 2024-12-26 | |
| dc.date.copyright | 2019-12-26 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-12-19 | |
| dc.identifier.citation | References
[1] Srivastava, L., & Kelly, T. (2005). The internet of things. International Telecommunication Union, Tech. Rep, 7. [2] Palmisano, S. J. (2008). A smarter planet: the next leadership agenda. IBM. November, 6, 1-8. [3]https://www.gartner.com/smarterwithgartner/5-trends-emerge-in-gartner-hype-cycle-for-emerging-technologies-2018/ [4] http://www.nationalstrategies.com/smart-cities [5] Abdmeziem, M. R., Tandjaoui, D., & Romdhani, I. (2016). Architecting the internet of things: state of the art. In Robots and Sensor Clouds (pp. 55-75). Springer, Cham. [6] Bogue, R. (2013). Recent developments in MEMS sensors: A review of applications, markets and technologies. Sensor Review, 33(4), 300-304. [7] Tanaka, M. (2007). An industrial and applied review of new MEMS devices features. Microelectronic Engineering, 84(5-8), 1341-1344. [8] Ciuti, G., Ricotti, L., Menciassi, A., & Dario, P. (2015). MEMS sensor technologies for human centred applications in healthcare, physical activities, safety and environmental sensing: a review on research activities in Italy. Sensors, 15(3), 6441-6468. [9] Sabato, A., Niezrecki, C., & Fortino, G. (2017). Wireless MEMS-based accelerometer sensor boards for structural vibration monitoring: a review. IEEE Sensors Journal, 17(2), 226-235. [10] Bandodkar, A. J., & Wang, J. (2014). Non-invasive wearable electrochemical sensors: a review. Trends in Biotechnology, 32(7), 363-371. [11] Ishida, H., Wada, Y., & Matsukura, H. (2012). Chemical sensing in robotic applications: A review. IEEE Sensors Journal, 12(11), 3163-3173. [12] Kumar, R., Al-Dossary, O., Kumar, G., & Umar, A. (2015). Zinc oxide nanostructures for NO 2 gas–sensor applications: a review. Nano-Micro Letters, 7(2), 97-120. [13] Wang, T., Huang, D., Yang, Z., Xu, S., He, G., Li, X., ... & Zhang, L. (2016). A review on graphene-based gas/vapor sensors with unique properties and potential applications. Nano-Micro Letters, 8(2), 95-119. [14] Guo, X. (2012). Surface plasmon resonance based biosensor technique: a review. Journal of Biophotonics, 5(7), 483-501. [15] Llandro, J., Palfreyman, J. J., Ionescu, A., & Barnes, C. H. W. (2010). Magnetic biosensor technologies for medical applications: a review. Medical & Biological Engineering & Computing, 48(10), 977-998. [16] Zhang, G. J., & Ning, Y. (2012). Silicon nanowire biosensor and its applications in disease diagnostics: a review. Analytica Chimica Acta, 749, 1-15. [17] Shao, Y., Wang, J., Wu, H., Liu, J., Aksay, I. A., & Lin, Y. (2010). Graphene based electrochemical sensors and biosensors: a review. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis, 22(10), 1027-1036. [18] Syu, Y. C., Hsu, W. E., & Lin, C. T. (2018). Field-Effect Transistor Biosensing: Devices and Clinical Applications. ECS Journal of Solid State Science and Technology, 7(7), Q3196-Q3207. [19] Mukhopadhyay, S. C. (2015). Wearable sensors for human activity monitoring: A review. IEEE Sensors Journal, 15(3), 1321-1330. [20] Ding, D., Cooper, R. A., Pasquina, P. F., & Fici-Pasquina, L. (2011). Sensor technology for smart homes. Maturitas, 69(2), 131-136. [21] Maraiya, K., Kant, K., & Gupta, N. (2011). Wireless sensor network: a review on data aggregation. International Journal of Scientific & Engineering Research, 2(4), 1-6. [22] Jovanov, E., Lords, A. D., Raskovic, D., Cox, P. G., Adhami, R., & Andrasik, F. (2003). Stress monitoring using a distributed wireless intelligent sensor system. IEEE Engineering in Medicine and Biology Magazine, 22(3), 49-55. [23] Darwish, A., & Hassanien, A. E. (2011). Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors, 11(6), 5561-5595. [24] Gardner, J. W., Guha, P. K., Udrea, F., & Covington, J. A. (2010). CMOS interfacing for integrated gas sensors: A review. IEEE Sensors Journal, 10(12), 1833-1848. [25] Shaeffer, D. K. (2013). MEMS inertial sensors: A tutorial overview. IEEE Communications Magazine, 51(4), 100-109. [26] Zahorian, J., Hochman, M., Xu, T., Satir, S., Gurun, G., Karaman, M., & Degertekin, F. L. (2011). Monolithic CMUT-on-CMOS integration for intravascular ultrasound applications. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 58(12), 2659-2667. [27] Gurun, G., Tekes, C., Zahorian, J., Xu, T., Satir, S., Karaman, M., ... & Degertekin, F. L. (2014). Single-chip CMUT-on-CMOS front-end system for real-time volumetric IVUS and ICE imaging. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 61(2), 239-250. [28] Li, H., Liu, X., Li, L., Mu, X., Genov, R., & Mason, A. (2017). CMOS electrochemical instrumentation for biosensor microsystems: A review. Sensors, 17(1), 74. [29] Gao, W., Emaminejad, S., Nyein, H. Y. Y., Challa, S., Chen, K., Peck, A., ... & Lien, D. H. (2016). Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature, 529(7587), 509. [30] Martín, A., Kim, J., Kurniawan, J. F., Sempionatto, J. R., Moreto, J. R., Tang, G., ... & Wang, J. (2017). Epidermal microfluidic electrochemical detection system: Enhanced sweat sampling and metabolite detection. ACS sensors, 2(12), 1860-1868. [31] Choi, J., Kang, D., Han, S., Kim, S. B., & Rogers, J. A. (2017). Thin, Soft, Skin‐Mounted Microfluidic Networks with Capillary Bursting Valves for Chrono‐Sampling of Sweat. Advanced healthcare materials, 6(5), 1601355. [32] Martinelli, E., Magna, G., Polese, D., Vergara, A., Schild, D., & Di Natale, C. (2015). Stable odor recognition by a neuro-adaptive electronic nose. Scientific reports, 5, 10960. [33] Chang, Y., Tang, N., Qu, H., Liu, J., Zhang, D., Zhang, H., ... & Duan, X. (2016). Detection of volatile organic compounds by self-assembled monolayer coated sensor array with concentration-independent fingerprints. Scientific reports, 6, 23970. [34] Wilson, A. D. (2012). Review of electronic-nose technologies and algorithms to detect hazardous chemicals in the environment. Procedia Technology, 1, 453-463. [35] Scott, S. M., James, D., & Ali, Z. (2006). Data analysis for electronic nose systems. Microchimica Acta, 156(3-4), 183-207. [36] Montuschi, P., Santonico, M., Mondino, C., Pennazza, G., Mantini, G., Martinelli, E., ... & Barnes, P. J. (2010). Diagnostic performance of an electronic nose, fractional exhaled nitric oxide, and lung function testing in asthma. Chest, 137(4), 790-796. [37] Aleixandre, M., Lozano, J., Gutiérrez, J., Sayago, I., Fernández, M. J., & Horrillo, M. C. (2008). Portable e-nose to classify different kinds of wine. Sensors and Actuators B: Chemical, 131(1), 71-76. [38] Gardner, J. W., Shin, H. W., Hines, E. L., & Dow, C. S. (2000). An electronic nose system for monitoring the quality of potable water. Sensors and Actuators B: Chemical, 69(3), 336-341. [39] Haddi, Z., Mabrouk, S., Bougrini, M., Tahri, K., Sghaier, K., Barhoumi, H., ... & Bouchikhi, B. (2014). E-Nose and e-Tongue combination for improved recognition of fruit juice samples. Food Chemistry, 150, 246-253. [40] Apetrei, C., Apetrei, I. M., Nevares, I., Del Alamo, M., Parra, V., Rodríguez-Méndez, M. L., & De Saja, J. A. (2007). Using an e-tongue based on voltammetric electrodes to discriminate among red wines aged in oak barrels or aged using alternative methods: correlation between electrochemical signals and analytical parameters. Electrochimica Acta, 52(7), 2588-2594. [41] Apetrei, C., Apetrei, I. M., Villanueva, S., De Saja, J. A., Gutiérrez-Rosales, F., & Rodriguez-Mendez, M. L. (2010). Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness. Analytica Chimica Acta, 663(1), 91-97. [42] Güntner, A. T., Koren, V., Chikkadi, K., Righettoni, M., & Pratsinis, S. E. (2016). E-nose sensing of low-ppb formaldehyde in gas mixtures at high relative humidity for breath screening of lung cancer?. ACS sensors, 1(5), 528-535. [43] Bergveld, P. (1970). Development of an ion-sensitive solid-state device for neurophysiological measurements. IEEE Transactions on Biomedical Engineering, (1), 70-71. [44] Gonçalves, D., Prazeres, D. M. F., Chu, V., & Conde, J. P. (2008). Detection of DNA and proteins using amorphous silicon ion-sensitive thin-film field effect transistors. Biosensors and Bioelectronics, 24(4), 545-551. [45] Uslu, F., Ingebrandt, S., Mayer, D., Böcker-Meffert, S., Odenthal, M., & Offenhäusser, A. (2004). Labelfree fully electronic nucleic acid detection system based on a field-effect transistor device. Biosensors and Bioelectronics, 19(12), 1723-1731. [46] Duarte-Guevara, C., Swaminathan, V. V., Reddy, B., Huang, J. C., Liu, Y. S., & Bashir, R. (2016). On-chip electrical detection of parallel loop-mediated isothermal amplification with DG-BioFETs for the detection of foodborne bacterial pathogens. RSC Advances, 6(106), 103872-103887. [47] Go, J., Nair, P. R., Reddy, B., Dorvel, B., Bashir, R., & Alam, M. A. (2010, December). Beating the Nernst limit of 59mV/pH with double-gated nano-scale field-effect transistors and its applications to ultra-sensitive DNA biosensors. In 2010 International Electron Devices Meeting (pp. 8-7). IEEE. [48] Garner, D. M., Bai, H., Georgiou, P., Constandinou, T. G., Reed, S., Shepherd, L. M., ... & Toumazou, C. (2010, February). A multichannel DNA SoC for rapid point-of-care gene detection. In 2010 IEEE International Solid-State Circuits Conference-(ISSCC) (pp. 492-493). IEEE. [49] Lee, I. K., Jeun, M., Jang, H. J., Cho, W. J., & Lee, K. H. (2015). A self-amplified transistor immunosensor under dual gate operation: highly sensitive detection of hepatitis B surface antigen. Nanoscale, 7(40), 16789-16797. [50] Kamahori, M., Ishige, Y., & Shimoda, M. (2007). A novel enzyme immunoassay based on potentiometric measurement of molecular adsorption events by an extended-gate field-effect transistor sensor. Biosensors and Bioelectronics, 22(12), 3080-3085. [51] Yano, M., Koike, K., Mukai, K., Onaka, T., Hirofuji, Y., Ogata, K. I., ... & Sasa, S. (2014). Zinc oxide ion‐sensitive field‐effect transistors and biosensors. Physica Status Solidi (a), 211(9), 2098-2104. [52] Bhalla, N., Di Lorenzo, M., Estrela, P., & Pula, G. (2017). Semiconductor technology in protein kinase research and drug discovery: sensing a revolution. Drug Discovery Today, 22(2), 204-209. [53] Adiguzel, Y., & Kulah, H. (2012). CMOS cell sensors for point-of-care diagnostics. Sensors, 12(8), 10042-10066. [54] Starodub, N. F., & Ogorodnijchuk, J. O. (2012). Immune biosensor based on the ISFETs for express determination of Salmonella typhimurium. Electroanalysis, 24(3), 600-606. [55] Nakata, S., Arie, T., Akita, S., & Takei, K. (2017). Wearable, flexible, and multifunctional healthcare device with an ISFET chemical sensor for simultaneous sweat pH and skin temperature monitoring. ACS sensors, 2(3), 443-448. [56] Jimenez-Jorquera, C., Orozco, J., & Baldi, A. (2010). ISFET based microsensors for environmental monitoring. Sensors, 10(1), 61-83. [57] Maruyama, Y., Terao, S., & Sawada, K. (2009). Label free CMOS DNA image sensor based on the charge transfer technique. Biosensors and Bioelectronics, 24(10), 3108-3112. [58] Rothberg, J. M., Hinz, W., Rearick, T. M., Schultz, J., Mileski, W., Davey, M., ... & Hoon, J. (2011). An integrated semiconductor device enabling non-optical genome sequencing. Nature, 475(7356), 348. [59] Shoorideh, K., & Chui, C. O. (2012). Optimization of the sensitivity of FET-based biosensors via biasing and surface charge engineering. IEEE Transactions on Electron Devices, 59(11), 3104-3110. [60] Shoorideh, K., & Chui, C. O. (2014). On the origin of enhanced sensitivity in nanoscale FET-based biosensors. Proceedings of the National Academy of Sciences, 111(14), 5111-5116. [61] Bard, A. J., & Faulkner, L. R. (2001). Fundamentals and applications. Electrochemical Methods, 2, 482. [62] Uno, S., Iio, M., Ozawa, H., & Nakazato, K. (2010). Full three-dimensional simulation of ion-sensitive field-effect transistor flatband voltage shifts due to DNA immobilization and hybridization. Japanese Journal of Applied Physics, 49(1S), 01AG07. [63] Kaisti, M. (2017). Detection principles of biological and chemical FET sensors. Biosensors and Bioelectronics, 98, 437-448. [64] Moser, N., Lande, T. S., Toumazou, C., & Georgiou, P. (2016). ISFETs in CMOS and emergent trends in instrumentation: A review. IEEE Sensors Journal, 16(17), 6496-6514. [65] Chen, S., Bomer, J. G., Carlen, E. T., & van den Berg, A. (2011). Al2O3/silicon nanoISFET with near ideal Nernstian response. Nano letters, 11(6), 2334-2341. [66] Knopfmacher, O., Tarasov, A., Fu, W., Wipf, M., Niesen, B., Calame, M., & Schonenberger, C. (2010). Nernst limit in dual-gated Si-nanowire FET sensors. Nano letters, 10(6), 2268-2274. [67] Spijkman, M. J., Brondijk, J. J., Geuns, T. C., Smits, E. C., Cramer, T., Zerbetto, F., ... & de Leeuw, D. M. (2010). Dual‐gate organic field‐effect transistors as potentiometric sensors in aqueous solution. Advanced Functional Materials, 20(6), 898-905. [68] Khamaisi, B., Vaknin, O., Shaya, O., & Ashkenasy, N. (2010). Electrical performance of silicon-on-insulator field-effect transistors with multiple top-gate organic layers in electrolyte solution. ACS nano, 4(8), 4601-4608. [69] Duarte-Guevara, C., Lai, F. L., Cheng, C. W., Reddy Jr, B., Salm, E., Swaminathan, V., ... & Bashir, R. (2014). Enhanced biosensing resolution with foundry fabricated individually addressable dual-gated ISFETs. Analytical chemistry, 86(16), 8359-8367. [70] Go, J., Nair, P. R., & Alam, M. A. (2012). Theory of signal and noise in double-gated nanoscale electronic p H sensors. Journal of Applied Physics, 112(3), 034516. [71] Huang, Y. J., Lin, C. C., Huang, J. C., Hsieh, C. H., Wen, C. H., Chen, T. T., ... & Liu, Y. S. (2015, December). High performance dual-gate ISFET with non-ideal effect reduction schemes in a SOI-CMOS bioelectrical SoC. In 2015 IEEE International Electron Devices Meeting (IEDM) (pp. 29-2). IEEE. [72] Chi, L. L., Chou, J. C., Chung, W. Y., Sun, T. P., & Hsiung, S. K. (2000). Study on extended gate field effect transistor with tin oxide sensing membrane. Materials Chemistry and Physics, 63(1), 19-23. [73] Ito, Y. (2000). Stability of ISFET and its new measurement protocol. Sensors and Actuators B: Chemical, 66(1-3), 53-55. [74] Khanna, V. K. (2013). Remedial and adaptive solutions of ISFET non-ideal behaviour. Sensor Review, 33(3), 228-237. [75] Shin, P. K. (2003). The pH-sensing and light-induced drift properties of titanium dioxide thin films deposited by MOCVD. Applied Surface Science, 214(1-4), 214-221. [76] Van Der Wal, P. D., Briand, D., Mondin, G., Jenny, S., Jeanneret, S., Millon, C., ... & De Rooij, N. F. (2004, October). High-k dielectrics for use as ISFET gate oxides. In SENSORS, 2004 IEEE (pp. 677-680). IEEE. [77] Lue, C. E., Yu, T. C., Yang, C. M., Pijanowska, D. G., & Lai, C. S. (2011). Optimization of urea-EnFET based on Ta2O5 layer with post annealing. Sensors, 11(5), 4562-4571. [78] Wu, C. L., Chou, J. C., Chung, W. Y., Sun, T. P., & Hsiung, S. K. (2000). Study on SnO2/Al/SiO2/Si ISFET with a metal light shield. Materials Chemistry and Physics, 63(2), 153-156. [79] Chiang, J. L., Chou, J. C., & Chen, Y. C. (2005). Study on light and temperature properties of aln ph-ion-sensitive field-effect transistor devices. Japanese Journal of Applied Physics, 44(7R), 4831. [80] Chin, Y. L., Chou, J. C., Sun, T. P., Chung, W. Y., & Hsiung, S. K. (2001). A novel pH sensitive ISFET with on chip temperature sensing using CMOS standard process. Sensors and Actuators B: Chemical, 76(1-3), 582-593. [81] Daniel, M., Janicki, M., & Napieralski, A. (2003, October). Simulation of ion sensitive transistors using a SPICE compatible model. In SENSORS, 2003 IEEE (Vol. 1, pp. 543-548). IEEE. [82] Barabash, P. R., Cobbold, R. S., & Wlodarski, W. B. (1987). Analysis of the threshold voltage and its temperature dependence in electrolyte-insulator-semiconductor field-effect transistors (EISFET's). IEEE Transactions on Electron Devices, 34(6), 1271-1282. [83] Matsumoto, K., Nakamura, T., Yusa, A., & Nagai, S. (1985). A new MOS phototransistor operating in a non-destructive readout mode. Japanese Journal of Applied Physics, 24(5A), L323. [84] Matsunaga, Y., Yamashita, H., Manabe, S., & Harada, N. (1991). A high-sensitivity MOS photo-transistor for area image sensor. IEEE Transactions on Electron Devices, 38(5), 1044-1047. [85] Woo, J., Chien, P. Y., Yang, F., Song, S. C., Chidambaram, C., Wang, J., & Yeap, G. (2014). Improved device variability in scaled MOSFETs with deeply retrograde channel profile. Microelectronics Reliability, 54(6-7), 1090-1095. [86] Khanna, V. K. (2017). Extreme-Temperature and Harsh-Environment Electronics; Physics, technology and applications. Extreme-Temperature and Harsh-Environment Electronics; Physics, technology and applications, by Khanna, Vinod Kumar. ISBN: 978-0-7503-1156-4. IOP ebooks. Bristol, UK: IOP Publishing, 2017. [87] Bergveld, P. (2003). Thirty years of ISFETOLOGY: What happened in the past 30 years and what may happen in the next 30 years. Sensors and Actuators B: Chemical, 88(1), 1-20. [88] Zhao, W., Bhushan, A., Santamaria, A. D., Simon, M. G., & Davis, C. E. (2008). Machine learning: A crucial tool for sensor design. Algorithms, 1(2), 130-152. [89] Hotel, O., Poli, J. P., Mer-Calfati, C., Scorsone, E., & Saada, S. (2018). A review of algorithms for SAW sensors e-nose based volatile compound identification. Sensors and Actuators B: Chemical, 255, 2472-2482. [90] Acevedo, F. J., Maldonado, S., Dominguez, E., Narvaez, A., & Lopez, F. (2007). Probabilistic support vector machines for multi-class alcohol identification. Sensors and Actuators B: Chemical, 122(1), 227-235. [91] Brudzewski, K., Osowski, S., & Markiewicz, T. (2004). Classification of milk by means of an electronic nose and SVM neural network. Sensors and Actuators B: Chemical, 98(2-3), 291-298. [92] Liu, M., Wang, M., Wang, J., & Li, D. (2013). Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators B: Chemical, 177, 970-980. [93] Argyri, A. A., Panagou, E. Z., Tarantilis, P. A., Polysiou, M., & Nychas, G. J. (2010). Rapid qualitative and quantitative detection of beef fillets spoilage based on Fourier transform infrared spectroscopy data and artificial neural networks. Sensors and Actuators B: Chemical, 145(1), 146-154. [94] Diamond, A., Schmuker, M., Berna, A. Z., Trowell, S., & Nowotny, T. (2016). Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system. Bioinspiration & Biomimetics, 11(2), 026002. [95] Glorot, X., Bordes, A., & Bengio, Y. (2011, June). Deep sparse rectifier neural networks. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (pp. 315-323). [96] Zeiler, M. D. (2012). ADADELTA: an adaptive learning rate method. arXiv preprint arXiv:1212.5701. [97] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [98] Qian, N. (1999). On the momentum term in gradient descent learning algorithms. Neural Networks, 12(1), 145-151. [99] Kiefer, J., & Wolfowitz, J. (1952). Stochastic estimation of the maximum of a regression function. The Annals of Mathematical Statistics, 23(3), 462-466. [100] Huang, X., Yu, H., Liu, X., Jiang, Y., Yan, M., & Wu, D. (2015). A dual-mode large-arrayed CMOS ISFET sensor for accurate and high-throughput pH sensing in biomedical diagnosis. IEEE Transactions on Biomedical Engineering, 62(9), 2224-2233. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74750 | - |
| dc.description.abstract | 由於物聯網的發展，次世代的感測器研究正在發生典範轉移。如今，多模態感測器已經在物聯網及人工智慧應用中扮演不可或缺的角色。為了更進一步降低功率及感測器尺寸，多模態感測元件將是未來感測技術的重要趨勢。因此，在本論文中，我們利用雙閘極離子感測場效電晶體成功實現多模態感測元件，成功同時量化酸鹼度，光及溫度三種物理變量。並提出虛擬感測器空間的模型來解釋多模態感測原理。首先，本論文提出利用時序產生虛擬感測器的理論，並解釋虛擬感測器空間與多重感測效應的關聯性。隨後，我們成功實現並驗證酸鹼度/光度雙重感測器以及酸鹼度/光/及溫度三重感測器，並且利用感測器的量測數據驗證我們所提出之虛擬感測空間的模型。利用本論文提出的虛擬感測器空間與多模態感測原理，我們可以將多模態感測器實現於單一感測元件中，並應用於感測器融合，物聯網以及人工智慧辨識等應用。 | zh_TW |
| dc.description.abstract | Internet-of-thing (IoT) has driven a paradigm shift to the next-generation sensor architecture. Nowadays, multi-modal sensors are essential for various IoT and AI applications. To achieve ultra-low power and size reduction, single-device-multi-sensor could be a promising direction of sensor technologies. Therefore, we have realized a machine learning-assisted multi-modal sensing device based on a dual-gate ion-sensitive field-effect transistor (DG-ISFET) and proposed a virtual sensor space model to effectively explain the fact of multi-sensing. In the first part, we propose a sequential method to generate virtual sensors and a virtual sensor space model to connect the physical sensing device and multi-sensing functionalities. Afterwards, we successfully realized and validated a pH/light dual-modal sensor and a pH/light/temperature 3-dimensional multi-modal sensor. In addition, the virtual sensor space model is also validated. With the proposed virtual sensor space model, multi-modalities sensing can be achieved on single device. It has great potential applying to sensor fusion and AI recognition technologies. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:06:56Z (GMT). No. of bitstreams: 1 ntu-108-D04943015-1.pdf: 4118855 bytes, checksum: 9918a5a8562e0ff263077bcc6d7cee08 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | Content
誌謝 I 摘要 II Abstract III Content IV List of Figures VIII List of Tables XI Chapter 1. Introduction 1 1-1 Background and Motivation of IoT Sensor Researches 1 1-2 Sensor Technology Review 3 1-3 The Structure of this Thesis 7 Chapter 2. Dual-gate ISFET Measurement and Characterization 9 2-1 Fundamental and Sensing Principle of ISFET Biochemical Sensors 10 2-2 ISFET Subtypes 14 2-2.1 Conventional ISFET 14 2-2.2 Dual-gate ISFET 15 2-3 Non-ideal Characteristics of ISFET 17 2-3.1 Photo-effect 17 2-3.2 Temperature Effect 18 2-4 DG-ISFET Design, Fabrication, and Measurement Setup 19 2-4.1 Design and Fabrication 19 2-4.2 Measurement Setup 22 2-4.3 pH and Light Measurement Sequences 23 2-4.4 Temperature Measurement Setup 24 2-5 Dual-gate ISFET Characterization 25 2-5.1 pH Sensing Discussion 25 2-5.2 Photo-response 30 2-5.3 Thermal Response 34 2-6 Chapter Conclusion 35 Chapter 3 The Concept of Virtual Sensor and Machine Learning Approaches 36 3-1 Introduction 36 3-2 Sequentially Generated Virtual Sensor 36 3-3 Virtual Sensor Space 39 3-4 Machine Learning Models and Settings 46 3-4.1 Support Vector Machine 47 3-4.2 Artificial Neural Network 48 Chapter 4 Dual-Modal Sensing ISFET Realization and Validation 50 4-1 Measured Data Analysis and Discussions 51 4-2 Visualized Data by Features 53 4-2.1 Data Pre-Processing 54 4-3 Modeling and Validation 56 4-3.1 Semi-Quantification Models 57 4-3.2 Quantification Models 60 4-3.3 Cross-Validation 63 4-3.4 Feature reduction 66 4-4 Chapter Conclusion 68 Chapter 5 Three-Dimensional Multi-Modal Sensing ISFET and Virtual Sensor Space Validation 70 5-1 Introduction 70 5-2 Sensor Measurement and Data Collection 71 5-2.1 Sensing Device Characteristics and Virtual Sensor Generation 73 5-3 Multi-Modal Sensing Model and Validation 75 5-3.1 Operational Orthogonality 77 5-3.2 Intrinsic Orthogonality 81 5-3.3 Effect of Neural Network Structure 84 5-4 Chapter Conclusion 86 Chapter 6 Conclusions and Future Works 88 References 91 | |
| dc.language.iso | en | |
| dc.subject | 多模態感測器 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 離子感測場效電晶體 | zh_TW |
| dc.subject | 物聯網感測器 | zh_TW |
| dc.subject | 元件物理 | zh_TW |
| dc.subject | ISFET | en |
| dc.subject | machine learning | en |
| dc.subject | Multi-modal sensor | en |
| dc.subject | device physics | en |
| dc.subject | IoT sensor | en |
| dc.title | 利用機器學習於單一電晶體上實現多重模態感測效果 | zh_TW |
| dc.title | A Machine Learning-assisted Multi-modality Sensor Based on a Single Transistor | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 林啟萬,吳安宇,鄭桂忠,劉怡劭,王玉麟 | |
| dc.subject.keyword | 多模態感測器,機器學習,離子感測場效電晶體,物聯網感測器,元件物理, | zh_TW |
| dc.subject.keyword | Multi-modal sensor,machine learning,ISFET,IoT sensor,device physics, | en |
| dc.relation.page | 108 | |
| dc.identifier.doi | 10.6342/NTU201904386 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-12-20 | |
| 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 |
| Appears in Collections: | 電子工程學研究所 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-108-1.pdf Restricted Access | 4.02 MB | Adobe PDF |
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