| 研究生: |
楊景翔 Yang, Ching-Hsiang |
|---|---|
| 論文名稱: |
氧化銦鎵鋅感光電晶體應用於類神經網絡及儲備池計算 Optoelectronic Characteristics of IGZO Phototransistor for Neuromorphic and Reservoir Computing |
| 指導教授: |
陳貞夙
Chen, Jen-Sue |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 材料科學及工程學系 Department of Materials Science and Engineering |
| 論文出版年: | 2023 |
| 畢業學年度: | 111 |
| 語文別: | 中文 |
| 論文頁數: | 114 |
| 中文關鍵詞: | 氧化物薄膜電晶體 、突觸電晶體 、光電電晶體 、儲備池計算 、類神經網絡 |
| 外文關鍵詞: | Oxide phototransistor, Neuromorphic computing, Reservoir computing |
| 相關次數: | 點閱:50 下載:0 |
| 分享至: |
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(1) Backus, J. Can programming be liberated from the von Neumann style? a functional style and its algebra of programs. Commun. ACM 1978, 21, 613–641. DOI: 10.1145/359576.359579.
(2) Schuman, C. D.; Kulkarni, S. R.; Parsa, M.; Mitchell, J. P.; Date, P.; Kay, B. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2022, 2, 10-19. DOI: 10.1038/s43588-021-00184-y.
(3) Marković, D.; Mizrahi, A.; Querlioz, D.; Grollier, J. Physics for neuromorphic computing. Nat. Rev. Phys. 2020, 2, 499-510. DOI: 10.1038/s42254-020-0208-2.
(4) Roy, K.; Jaiswal, A.; Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 2019, 575, 607-617. DOI: 10.1038/s41586-019-1677-2.
(5) Yang, H.; Lam, K.-Y.; Xiao, L.; Xiong, Z.; Hu, H.; Niyato, D.; Vincent Poor, H. Lead federated neuromorphic learning for wireless edge artificial intelligence. Nat. Commun. 2022, 13, 4269. DOI: 10.1038/s41467-022-32020-w.
(6) Li, B.; Chen, P.; Liu, H.; Guo, W.; Cao, X.; Du, J.; Zhao, C.; Zhang, J. Random sketch learning for deep neural networks in edge computing. Nat. Comput. Sci. 2021, 1, 221-228. DOI: 10.1038/s43588-021-00039-6.
(7) Zhou, F.; Chai, Y. Near-sensor and in-sensor computing. Nat. Electron. 2020, 3, 664-671. DOI: 10.1038/s41928-020-00501-9.
(8) Wan, T.; Shao, B.; Ma, S.; Zhou, Y.; Li, Q.; Chai, Y. In-Sensor Computing: Materials, Devices, and Integration Technologies. Adv. Mater. 2022, 9, 2203830. DOI: 10.1002/adma.202203830.
(9) Shang, Z.-W.; Hsu, H.-H.; Zheng, Z.-W.; Cheng, C.-H. Progress and challenges in p-type oxide-based thin film transistors. Nanotechnol. Rev. 2019, 8, 422-443. DOI: doi:10.1515/ntrev-2019-0038.
(10) Choi, J. Y.; Lee, S. Y. Comprehensive review on the development of high mobility in oxide thin film transistors. J. Korean Phys. Soc. 2017, 71, 516-527. DOI: 10.3938/jkps.71.516.
(11) Thomas, S. R.; Pattanasattayavong, P.; Anthopoulos, T. D. Solution-processable metal oxide semiconductors for thin-film transistor applications. Chem. Soc. Rev. 2013, 42, 6910-6923. DOI: 10.1039/C3CS35402D.
(12) Liu, J.; Xia, F.; Xiao, D.; García de Abajo, F. J.; Sun, D. Semimetals for high-performance photodetection. Nat. Mater. 2020, 19, 830-837. DOI: 10.1038/s41563-020-0715-7.
(13) Miller, D. A. B. Energy consumption in optical modulators for interconnects. Opt. Express 2012, 20, A293-A308. DOI: 10.1364/OE.20.00A293.
(14) Chow, P. C. Y.; Matsuhisa, N.; Zalar, P.; Koizumi, M.; Yokota, T.; Someya, T. Dual-gate organic phototransistor with high-gain and linear photoresponse. Nat. Commun. 2018, 9, 4546. DOI: 10.1038/s41467-018-06907-6.
(15) Cox, David D.; Dean, T. Neural Networks and Neuroscience-Inspired Computer Vision. Curr. Biol. 2014, 24, R921-R929. DOI: 10.1016/j.cub.2014.08.026.
(16) Harikesh, P. C.; Yang, C.-Y.; Tu, D.; Gerasimov, J. Y.; Dar, A. M.; Armada-Moreira, A.; Massetti, M.; Kroon, R.; Bliman, D.; Olsson, R.; Stavrinidou, E.; Berggren, M.; Fabiano, S. Organic electrochemical neurons and synapses with ion mediated spiking. Nat. Commun. 2022, 13, 901. DOI: 10.1038/s41467-022-28483-6.
(17) Pereda, A. E. Electrical synapses and their functional interactions with chemical synapses. Nat. Rev. Neurosci. 2014, 15, 250-263. DOI: 10.1038/nrn3708.
(18) Yang, J.-Q.; Wang, R.; Ren, Y.; Mao, J.-Y.; Wang, Z.-P.; Zhou, Y.; Han, S.-T. Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems. Adv. Mater. 2020, 32, 2003610. DOI: 10.1002/adma.202003610.
(19) Pearce, J. M. S. Sir Charles Scott Sherrington (1857–1952) and the synapse. J. Neurol. Neurosurg. Psychiatry 2004, 75, 544-544. DOI: 10.1136/jnnp.2003.017921.
(20) Zucker, R. S.; Regehr, W. G. Short-Term Synaptic Plasticity. Annu. Rev. Physiol. 2002, 64, 355-405. DOI: 10.1146/annurev.physiol.64.092501.114547.
(21) Citri, A.; Malenka, R. C. Synaptic Plasticity: Multiple Forms, Functions, and Mechanisms. Neuropsychopharmacology 2008, 33, 18-41. DOI: 10.1038/sj.npp.1301559.
(22) Abbott, L. F.; Regehr, W. G. Synaptic computation. Nature 2004, 431, 796-803. DOI: 10.1038/nature03010.
(23) Chen, B.; Sun, S.; Fan, S.; Liu, X.; Li, Q.; Su, J. Low-Cost Fabricated MgSnO Electrolyte-Gated Synaptic Transistor with Dual Modulation of Excitation and Inhibition. Adv. Electron. Mater. 2022, 8, 2200864. DOI: 10.1002/aelm.202200864.
(24) Yang, Y.; Calakos, N. Presynaptic long-term plasticity. Front. Synaptic Neurosci. 2013, 5, 8. DOI: 10.3389/fnsyn.2013.00008.
(25) Collingridge, G. L.; Kehl, S. J.; McLennan, H. Excitatory amino acids in synaptic transmission in the Schaffer collateral-commissural pathway of the rat hippocampus. J. Physiol. 1983, 334, 33-46. DOI: 10.1113/jphysiol.1983.sp014478.
(26) Bin Ibrahim, M. Z.; Benoy, A.; Sajikumar, S. Long-term plasticity in the hippocampus: maintaining within and ‘tagging’ between synapses. FEBS J. 2022, 289, 2176-2201. DOI: 10.1111/febs.16065.
(27) Krogh, A. What are artificial neural networks? Nat. Biotechnol. 2008, 26, 195-197. DOI: 10.1038/nbt1386.
(28) Basheer, I. A.; Hajmeer, M. Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 2000, 43, 3-31. DOI: 10.1016/S0167-7012(00)00201-3.
(29) Choi, S.; Jang, S.; Moon, J.-H.; Kim, J. C.; Jeong, H. Y.; Jang, P.; Lee, K.-J.; Wang, G. A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems. NPG Asia Mater. 2018, 10, 1097-1106. DOI: 10.1038/s41427-018-0101-y.
(30) Tanaka, G.; Yamane, T.; Héroux, J. B.; Nakane, R.; Kanazawa, N.; Takeda, S.; Numata, H.; Nakano, D.; Hirose, A. Recent advances in physical reservoir computing: A review. Neural Netw. 2019, 115, 100-123. DOI: 10.1016/j.neunet.2019.03.005.
(31) Jaeger, H.; Haas, H. Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication. Science 2004, 304, 78-80. DOI: doi:10.1126/science.1091277.
(32) Maass, W.; Natschläger, T.; Markram, H. Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations. Neural Comput. 2002, 14, 2531-2560. DOI: 10.1162/089976602760407955.
(33) Lee, J. S.; Lee, S.; Noh, T. W. Resistive switching phenomena: A review of statistical physics approaches. Appl. Phys. Rev. 2015, 2, 031303. DOI: 10.1063/1.4929512.
(34) Lany, S.; Zunger, A. Anion vacancies as a source of persistent photoconductivity in II-VI and chalcopyrite semiconductors. Phys. Rev. B 2005, 72, 035215. DOI: 10.1103/PhysRevB.72.035215.
(35) Jeon, S.; Ahn, S.-E.; Song, I.; Kim, C. J.; Chung, U. I.; Lee, E.; Yoo, I.; Nathan, A.; Lee, S.; Ghaffarzadeh, K.; Robertson, J.; Kim, K. Gated three-terminal device architecture to eliminate persistent photoconductivity in oxide semiconductor photosensor arrays. Nat. Mater. 2012, 11, 301-305. DOI: 10.1038/nmat3256.
(36) Rieke, F.; Rudd, M. E. The Challenges Natural Images Pose for Visual Adaptation. Neuron 2009, 64, 605-616. DOI: 10.1016/j.neuron.2009.11.028.
(37) Schneeweis, D. M.; Schnapf, J. L. The Photovoltage of Macaque Cone Photoreceptors: Adaptation, Noise, and Kinetics. J. Neurosci. 1999, 19, 1203-1216. DOI: 10.1523/jneurosci.19-04-01203.1999.
(38) Khan, L. U. Visible light communication: Applications, architecture, standardization and research challenges. Digit. Commun. Netw. 2017, 3, 78-88. DOI: 10.1016/j.dcan.2016.07.004.
校內:2028-08-07公開