本研究成功以製備出之ZTO元件模擬多種突觸可塑性之特性，並探討金奈米粒子的添加與否對於突觸量測時之結果影響。首先，針對ZTO及ZTO/AuNPs(7L)兩元件之轉換特性曲線及TFT性質做比較，並說明遲滯的產生源自於缺陷對電子的捕捉，而金奈米粒子的加入可以增加缺陷，因而於ID-VG掃幅量測有較大的遲滯窗產生。突觸操作部分，先由施加單一電脈衝成功仿擬突觸最基本性質，並更進一步比較兩元件於負偏壓電脈衝及正偏壓電脈衝下之捕捉電子及釋放電子的能力，結果顯示ZTO及ZTO/AuNPs(7L)的捕捉電子能力相當，但ZTO/AuNPs(7L)的釋放電子能力優於ZTO，推測因為金奈米粒子的加入，除了ZTO與SiO2的界面存在缺陷之外，金奈米粒子與ZTO的界面也會有缺陷，因而使ZTO/AuNPs(7L)的釋放電子能力有所提升。
增益與抑制循環、成對脈衝增益、動態濾波器以及由短期可塑性轉變為長期可塑性之操作皆由單一刺激(電脈衝)進行，以上操作之結果皆為ZTO/AuNPs(7L)元件有比ZTO更貼近生物突觸的趨勢。本研究還引入光脈衝作為另一個刺激來源，藉由調節電脈衝與光脈衝之時序關係，實現更多元的突觸權重調控。由於光脈衝與負偏壓電脈衝對於元件都屬於增益的效果，因此由光脈衝與負偏壓電脈衝形成之突觸權重(ΔW)皆為正的，而正偏壓電脈衝對於元件為抑制的效果，此抑制效果會與光脈衝之增益效果相抗衡，故由光脈衝與正偏壓電脈衝形成之突觸權重(ΔW)則正負皆有可能。由於本研究於操作時之脈衝時間偏長，汲極偏壓偏高，因此兩元件於操作時的能耗便相當大。

In this study, we successfully emulate the characteristics of various synaptic plasticity with the prepared zinc tin oxide (ZTO) thin film transistor. Gold nanoparticles are added between ZTO and dielectric layer (SiO2) to investigate the influence of synaptic behaviors. First, the transfer characteristic curves of ZTO and ZTO/AuNPs(7L) devices are exhibited, the hysteresis window is caused by trapping electrons at interface defects between ZTO and SiO2. Apart from interface defects between ZTO and SiO2, the interface between gold nanoparticles and ZTO also have defects, which improves the ability of ZTO/AuNPs(7L) to trap/detrap electrons. In synaptic operation part, the application of single electrical pulse successfully emulate the basic property of synapse. The cycle of potentiation and depression, paired pulse facilitation, dynamic filter are all performed by electrical pulse. All above results of ZTO/AuNPs(7L) are better than ZTO and close to biological synapse. This study also introduces 405 nm laser light pulse as another source of stimulation. By adjusting the sequence of electrical pulse and light pulse, more diverse synaptic weight can be achieved. Synergistic effect is shown by applying negative electrical pulse and light pulse simultaneously. As for the energy consumption, the pulse duration time of this study is too long, and the drain voltage is also high, so the energy consumption of our two devices during synaptic operation are quite large.

(1) Yu, F.; Zhu, L. Q.; Xiao, H.; Gao, W. T.; Guo, Y. B. Restickable Oxide Neuromorphic Transistors with Spike-Timing-Dependent Plasticity and Pavlovian Associative Learning Activities. Advanced Functional Materials 2018, 28 (44), DOI: 10.1002/adfm.201804025.
(2) Yu, F.; Zhu, L. Q.; Gao, W. T.; Fu, Y. M.; Xiao, H.; Tao, J.; Zhou, J. M. Chitosan-Based Polysaccharide-Gated Flexible Indium Tin Oxide Synaptic Transistor with Learning Abilities. ACS Appl Mater Interfaces 2018, 10 (19), 16881-16886, DOI: 10.1021/acsami.8b03274.
(3) Yu, H.; Wei, H.; Gong, J.; Han, H.; Ma, M.; Wang, Y.; Xu, W. Evolution of Bio-Inspired Artificial Synapses: Materials, Structures, and Mechanisms. Small 2021, 17 (9), e2000041, DOI: 10.1002/smll.202000041.
(4) Indiveri, G.; Liu, S.-C. Memory and Information Processing in Neuromorphic Systems. Proceedings of the IEEE 2015, 103 (8), 1379-1397, DOI: 10.1109/jproc.2015.2444094.
(5) He, Y.; Yang, Y.; Nie, S.; Liu, R.; Wan, Q. Electric-double-layer transistors for synaptic devices and neuromorphic systems. Journal of Materials Chemistry C 2018, 6 (20), 5336-5352, DOI: 10.1039/c8tc00530c.
(6) Lv, D.; Yang, Q.; Chen, Q.; Chen, J.; Lai, D.; Chen, H.; Guo, T. All-metal oxide synaptic transistor with modulatable plasticity. Nanotechnology 2020, 31 (6), 065201, DOI: 10.1088/1361-6528/ab5080.
(7) Lee, J.; Lim, S.; Kwak, M.; Song, J.; Hwang, H. Understanding of proton induced synaptic behaviors in three-terminal synapse device for neuromorphic systems. Nanotechnology 2019, 30 (25), 255202, DOI: 10.1088/1361-6528/ab0b97.
(8) Dai, S.; Zhao, Y.; Wang, Y.; Zhang, J.; Fang, L.; Jin, S.; Shao, Y.; Huang, J. Recent Advances in Transistor‐Based Artificial Synapses. Advanced Functional Materials 2019, 29 (42), DOI: 10.1002/adfm.201903700.
(9) Lin, T.-R.; Shih, L.-C.; Cheng, P.-J.; Chen, K.-T.; Chen, J.-S. Violet-light stimulated synaptic and learning functions in a zinc–tin oxide photoelectric transistor for neuromorphic computation. RSC Advances 2020, 10 (70), 42682-42687, DOI: 10.1039/d0ra08777g.
(10) Nathan, A.; Lee, S.; Jeon, S.; Robertson, J. Amorphous Oxide Semiconductor TFTs for Displays and Imaging. Journal of Display Technology 2014, 10 (11), 917-927, DOI: 10.1109/jdt.2013.2292580.
(11) Fortunato, E.; Barquinha, P.; Martins, R. Oxide semiconductor thin-film transistors: a review of recent advances. Adv Mater 2012, 24 (22), 2945-86, DOI: 10.1002/adma.201103228.
(12) Lee, M.; Lee, W.; Choi, S.; Jo, J. W.; Kim, J.; Park, S. K.; Kim, Y. H. Brain-Inspired Photonic Neuromorphic Devices using Photodynamic Amorphous Oxide Semiconductors and their Persistent Photoconductivity. Adv Mater 2017, 29 (28), DOI: 10.1002/adma.201700951.
(13) Bae, J.; Jeong, I.; Lee, S. Wavelength-dependent Optical Instability Mechanisms and Decay Kinetics in Amorphous Oxide Thin-Film Devices. Sci Rep 2019, 9 (1), 2920, DOI: 10.1038/s41598-019-39744-8.
(14) John, R. A.; Liu, F.; Chien, N. A.; Kulkarni, M. R.; Zhu, C.; Fu, Q.; Basu, A.; Liu, Z.; Mathews, N. Synergistic Gating of Electro-Iono-Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity. Adv Mater 2018, 30 (25), e1800220, DOI: 10.1002/adma.201800220.
(15) Squire, L.; Berg, D.; Bloom, F.; Lac, S. d.; Ghosh, A.; Spitzer, N. Fundamental Neuroscience (3rd edition). 2008.
(16) Characterizing a neural action potential in space and time. https://physics.stackexchange.com/questions/287091/characterizing-a-neural-actionpotential-in-space-and-time.
(17) Voglis, G.; Tavernarakis, N. The role of synaptic ion channels in synaptic plasticity. EMBO Rep 2006, 7 (11), 1104-10, DOI: 10.1038/sj.embor.7400830.
(18) 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.
(19) Li, J.; Yang, Y.-H.; Chen, Q.; Zhu, W.-Q.; Zhang, J.-H. Aqueous-solution-processed proton-conducting carbon nitride/polyvinylpyrrolidone composite electrolytes for low-power synaptic transistors with learning and memory functions. Journal of Materials Chemistry C 2020, 8 (12), 4065-4072, DOI: 10.1039/c9tc05765j.
(20) Fu, W.-H.; Li, J.; Jiang, D.-L.; Yang, Y.-H.; Chen, Q.; Zhu, W.-Q.; Zhang, J.-H. Proton conducting C3N4/Chitosan composite electrolytes based InZnO thin film transistor for artificial synapse. Organic Electronics 2020, 85, DOI: 10.1016/j.orgel.2020.105870.
(21) Zhu, L. Q.; Wan, C. J.; Guo, L. Q.; Shi, Y.; Wan, Q. Artificial synapse network on inorganic proton conductor for neuromorphic systems. Nat Commun 2014, 5, 3158, DOI: 10.1038/ncomms4158.
(22) Yang, C. S.; Shang, D. S.; Liu, N.; Shi, G.; Shen, X.; Yu, R. C.; Li, Y. Q.; Sun, Y. A Synaptic Transistor based on Quasi-2D Molybdenum Oxide. Adv Mater 2017, 29 (27), DOI: 10.1002/adma.201700906.
(23) Brown, R. E. Donald O. Hebb and the Organization of Behavior: 17 years in the writing. Mol Brain 2020, 13 (1), 55, DOI: 10.1186/s13041-020-00567-8.
(24) Yoneyama, M.; Fukushima, Y.; Tsukada, M.; Aihara, T. Spatiotemporal characteristics of synaptic EPSP summation on the dendritic trees of hippocampal CA1 pyramidal neurons as revealed by laser uncaging stimulation. Cogn Neurodyn 2011, 5 (4), 333-42, DOI: 10.1007/s11571-011-9158-9.
(25) Kim, M. K.; Park, Y.; Kim, I. J.; Lee, J. S. Emerging Materials for Neuromorphic Devices and Systems. iScience 2020, 23 (12), 101846, DOI: 10.1016/j.isci.2020.101846.
(26) Cheng, Y.; Shan, K.; Xu, Y.; Yang, J.; He, J.; Jiang, J. Hardware implementation of photoelectrically modulated dendritic arithmetic and spike-timing-dependent plasticity enabled by an ion-coupling gate-tunable vertical 0D-perovskite/2D-MoS2 hybriddimensional van der Waals heterostructure. Nanoscale 2020, 12 (42), 21798-21811, DOI: 10.1039/d0nr04950f.
(27) Yang, Y.; Wen, J.; Guo, L.; Wan, X.; Du, P.; Feng, P.; Shi, Y.; Wan, Q. Long-Term Synaptic Plasticity Emulated in Modified Graphene Oxide Electrolyte Gated IZO-Based Thin-Film Transistors. ACS Appl Mater Interfaces 2016, 8 (44), 30281-30286, DOI: 10.1021/acsami.6b08515.
(28) Guo, L. Q.; Han, H.; Zhu, L. Q.; Guo, Y. B.; Yu, F.; Ren, Z. Y.; Xiao, H.; Ge, Z. Y.; Ding, J. N. Oxide Neuromorphic Transistors Gated by Polyvinyl Alcohol Solid Electrolytes with Ultralow Power Consumption. ACS Appl Mater Interfaces 2019, 11 (31), 28352-28358, DOI: 10.1021/acsami.9b05717.
(29) Kim, S.; Choi, B.; Lim, M.; Yoon, J.; Lee, J.; Kim, H. D.; Choi, S. J. Pattern Recognition Using Carbon Nanotube Synaptic Transistors with an Adjustable Weight Update Protocol. ACS Nano 2017, 11 (3), 2814-2822, DOI: 10.1021/acsnano.6b07894.
(30) Choi, Y.; Kim, J. H.; Qian, C.; Kang, J.; Hersam, M. C.; Park, J. H.; Cho, J. H. Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors. ACS Appl Mater Interfaces 2020, 12 (4), 4707-4714, DOI: 10.1021/acsami.9b17742.
(31) Yang, C. S.; Shang, D. S.; Liu, N.; Fuller, E. J.; Agrawal, S.; Talin, A. A.; Li, Y. Q.; Shen, B. G.; Sun, Y. All‐Solid‐State Synaptic Transistor with Ultralow Conductance for Neuromorphic Computing. Advanced Functional Materials 2018, 28 (42), DOI: 10.1002/adfm.201804170.
(32) Cho, S. W.; Kwon, S. M.; Lee, M.; Jo, J.-W.; Heo, J. S.; Kim, Y.-H.; Cho, H. K.; Park, S. K. Multi-spectral gate-triggered heterogeneous photonic neuro-transistors for powerefficient brain-inspired neuromorphic computing. Nano Energy 2019, 66, DOI: 10.1016/j.nanoen.2019.104097.