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Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing

Liubin Yang1, 2, Xiushuo Gu1, 2, Min Zhou2, Jianya Zhang4, Yonglin Huang1, and Yukun Zhao2, 3,

+ Author Affiliations

 Corresponding author: Yonglin Huang, huangyl@njupt.edu.cn; Yukun Zhao, ykzhao2017@sinano.ac.cn

DOI: 10.1088/1674-4926/24050037

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Abstract: Synaptic nano-devices have powerful capabilities in logic, memory and learning, making them essential components for constructing brain-like neuromorphic computing systems. Here, we have successfully developed and demonstrated a synaptic nano-device based on Ga2O3 nanowires with low energy consumption. Under 255 nm light stimulation, the biomimetic synaptic nano-device can stimulate various functionalities of biological synapses, including pulse facilitation, peak time-dependent plasticity and memory learning ability. It is found that the artificial synaptic device based on Ga2O3 nanowires can achieve an excellent "learning−forgetting−relearning" functionality. The transition from short-term memory to long-term memory and retention of the memory level after the stepwise learning can attribute to the great relearning functionality of Ga2O3 nanowires. Furthermore, the energy consumption of the synaptic nano-device can be lower than 2.39 × 10‒11 J for a synaptic event. Moreover, our device demonstrates exceptional stability in long-term stimulation and storage. In the application of neural morphological computation, the accuracy of digit recognition exceeds 90% after 12 training sessions, indicating the strong learning capability of the cognitive system composed of this synaptic nano-device. Therefore, our work paves an effective way for advancing hardware-based neural morphological computation and artificial intelligence systems requiring low power consumption.

Key words: Ga2O3 nanowiressynaptic nano-devicelow energy consumptionneural network



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Fig. 1.  (Color online) (a) Grow the GaN nanowires (NWs) on Si substrate. (b) Ga2O3 nanowires formed after oxidation. (c) Transfer the Ga2O3 nanowires to a solution. (d) Transfer Ga2O3 nanowires to the electrodes. (e) Top-view SEM image of the synaptic device based on Ga2O3 nanowires. (f) Top-view SEM image of Ga2O3 nanowires. (g) Side-view AC-STEM image and high-resolution EDX mapping images of the Ga2O3 nanowires. Schematic diagrams of (h) two adjacent neurons and (i) a biological synapse.

Fig. 2.  (Color online) (a) Current‒time (It) curve of the device when subjected to a single light pulse stimulation (0.16 mW·cm‒2) at 255 nm for 1 s. (b) It curve of the device under two consecutive light pulses at 255 nm. (c) The decay time as a function of the number of light pulses. (d) The It curve of the device under 5 continuous light pulses with an interval of 5 s at a constant bias voltage of 8 V.

Fig. 3.  (Color online) (a) EPSC of the synaptic device at different frequencies triggered by two consecutive 255 nm light pulses of 0.16 mW·cm‒2. (b) EPSC of a synaptic device stimulated by 10 consecutive 255 nm light pulses at different optical powers. (c) EPSC of synaptic device at various pulse numbers under an optical power density of 0.16 mW·cm‒2. (d) EPSC of synaptic device stimulated by 5 and 2 Hz light pulses.

Fig. 4.  (Color online) (a) EPSC of a synaptic device stimulated by two cycles of consecutive 5-light pulses at 5 s intervals (255 nm, 0.16 mW·cm‒2). Synaptic weight results of the synaptic device stimulated by (b) different numbers and (c) different frequencies of light pulses. (d) Synaptic weight results of the artificial device stimulated by light pulses with various optical power densities.

Fig. 5.  (Color online) (a) Equivalent circuit model of the synaptic nano-device. (b) IV curve of the synaptic nano-device. Schematic energy band diagrams of the Ga2O3 NWs (c) in dark, (d) under the first light stimulation, (e) without light stimulation, and (f) under the second light stimulation.

Fig. 6.  (Color online) (a) Schematic diagram of an ANN simulation using 784 × 100 × 10 synaptic weights. (b) Schematic of a neuron node. (c) Experimental data of LTD/LTP characteristics triggered by optical pulses and their corresponding fitting curves. (d) Simulate the accuracy of different training sessions. (e) The results of 20 digital images randomly selected from the MNIST database for identification.

Table 1.   Relevant parameters for the artificial neural network (ANN) training.

Parameters A B Fitted non-linear data
LTP 0.4 1.0894 2.91
LTD ‒0.65 ‒0.2734 ‒1.88
DownLoad: CSV
[1]
Lee T J, Kim S K, Seong T Y. Sputtering-deposited amorphous SrVOx-based memristor for use in neuromorphic computing. Sci Rep, 2020, 10, 5761 doi: 10.1038/s41598-020-62642-3
[2]
Wang J Q, Mao S S, Zhu S H, et al. Biomemristors-based synaptic devices for artificial intelligence applications. Org Electron, 2022, 106, 106540 doi: 10.1016/j.orgel.2022.106540
[3]
Feng Z Y, Yu J R, Wei Y C, et al. Tribo-ferro-optoelectronic neuromorphic transistor of α-In2Se3. Brain-X, 2023, 1, e24 doi: 10.1002/brx2.24
[4]
Song L K, Liu P Y, Pei J F, et al. Spiking neurons with neural dynamics implemented using stochastic memristors. Adv Electron Mater, 2024, 10, 2300564 doi: 10.1002/aelm.202300564
[5]
Tian B B, Xie Z Z, Chen L Q, et al. Ultralow-power in-memory computing based on ferroelectric memcapacitor network. Exploration, 2023, 3, 20220126 doi: 10.1002/EXP.20220126
[6]
Jiang S, Nie S, He Y, et al. Emerging synaptic devices: From two-terminal memristors to multiterminal neuromorphic transistors. Mater Today Nano, 2019, 8, 100059 doi: 10.1016/j.mtnano.2019.100059
[7]
Shainline J M, Buckley S M, Mirin R P, et al. Superconducting optoelectronic circuits for neuromorphic computing. Phys Rev Applied, 2017, 7, 034013 doi: 10.1103/PhysRevApplied.7.034013
[8]
Drachman D A. Do we have brain to spare? Neurology, 2005, 64, 2004 doi: 10.1212/01.WNL.0000166914.38327.BB
[9]
Monalisha P, Li S Y, Bhat S G, et al. Synaptic behavior of Fe3O4-based artificial synapse by electrolyte gating for neuromorphic computing. J Appl Phys, 2023, 133, 084901 doi: 10.1063/5.0120854
[10]
Zhang Y C, Liu L, Tu B, et al. An artificial synapse based on molecular junctions. Nat Commun, 2023, 14, 247 doi: 10.1038/s41467-023-35817-5
[11]
Zhao J S, Zheng S T, Zhou L W, et al. An artificial optoelectronic synapse based on MoOx film. Nanotechnology, 2023, 34, 145201 doi: 10.1088/1361-6528/acb217
[12]
Kwon J Y, Kim J E, Kim J S, et al. Artificial sensory system based on memristive devices. Exploration, 2024, 4, 20220162 doi: 10.1002/EXP.20220162
[13]
Zhang S, Xu W T. All-printed ultra-flexible organic nanowire artificial synapses. J Mater Chem C, 2020, 8, 11138 doi: 10.1039/D0TC02172E
[14]
Hirayama H. Research status and prospects of deep ultraviolet devices. J Semicond, 2019, 40, 120301 doi: 10.1088/1674-4926/40/12/120301
[15]
He T, Zhang X D, Ding X Y, et al. Broadband ultraviolet photodetector based on vertical Ga2O3/GaN nanowire array with high responsivity. Adv Opt Mater, 2019, 7, 1801653 doi: 10.1002/adom.201801653
[16]
Yoon Y, Kim Y, Hwang W S, et al. Biological UV photoreceptors-inspired Sn-doped polycrystalline β-Ga2O3 optoelectronic synaptic phototransistor for neuromorphic computing. Adv Electron Mater, 2023, 9, 2300098 doi: 10.1002/aelm.202300098
[17]
Lin X H, Long H T, Ke S, et al. Indium-gallium-zinc-oxide-based photoelectric neuromorphic transistors for spiking morse coding. Chin Phys Lett, 2022, 39, 068501 doi: 10.1088/0256-307X/39/6/068501
[18]
Sato K, Hayashi Y, Masaoka N, et al. High-temperature operation of gallium oxide memristors up to 600 K. Sci Rep, 2023, 13, 1261 doi: 10.1038/s41598-023-28075-4
[19]
Wang S P, He C L, Tang J, et al. Electronic synapses based on ultrathin quasi-two-dimensional gallium oxide memristor. Chin Phys B, 2019, 28, 017304 doi: 10.1088/1674-1056/28/1/017304
[20]
Zhou R F, Zhang W X, Cong H F, et al. Metal oxide semiconductor nanowires enabled air-stable ultraviolet-driven synaptic transistors for artificial vision. Mat Sci Semicon Proc, 2023, 158, 107344 doi: 10.1016/j.mssp.2023.107344
[21]
Huang C H, Wu C Y, Lin Y F, et al. Wet-etching-boosted charge storage in 1D nitride-based systems for imitating biological synaptic behaviors. Adv Funct Mater, 2023, 33, 2306030 doi: 10.1002/adfm.202306030
[22]
Chen X, Chen B K, Jiang B, et al. Nanowires for UV–vis–IR optoelectronic synaptic devices. Adv Funct Mater, 2023, 33, 2208807 doi: 10.1002/adfm.202208807
[23]
Huang F, Fang F E, Zheng Y, et al. Visible-light stimulated synaptic plasticity in amorphous indium-gallium-zinc oxide enabled by monocrystalline double perovskite for high-performance neuromorphic applications. Nano Res, 2023, 16, 1304 doi: 10.1007/s12274-022-4806-4
[24]
Li J, Wen S K, Jiang D L, et al. Fully solution-processed InSnO/HfGdOx thin-film transistor for light-stimulated artificial synapse. Flex Print Electron, 2022, 7, 014006 doi: 10.1088/2058-8585/ac4bb2
[25]
Wang Y Q, Wang W X, Zhang C W, et al. A digital‒analog integrated memristor based on a ZnO NPs/CuO NWs heterostructure for neuromorphic computing. ACS Appl Electron Mater, 2022, 4, 3525 doi: 10.1021/acsaelm.2c00495
[26]
Guo T, Zhang B Z, Wang X Y, et al. Broadband optoelectronic synapse enables compact monolithic neuromorphic machine vision for information processing. Adv Funct Mater, 2023, 33, 2303879 doi: 10.1002/adfm.202303879
[27]
Li J X, Dwivedi P, Kumar K S, et al. Growing perovskite quantum dots on carbon nanotubes for neuromorphic optoelectronic computing. Adv Electron Mater, 2021, 7, 2000535 doi: 10.1002/aelm.202000535
[28]
Liu J S, Li Z J, Jia C H, et al. Artificial synapse based on 1, 4-diphenylbutadiyne with femtojoule energy consumption. Phys Chem Chem Phys, 2023, 25, 5453 doi: 10.1039/D2CP05417E
[29]
Wang J Y, Wan C J, Wan Q. Dual-gate IGZO-based neuromorphic transistors with stacked Al2O3/chitosan gate dielectrics. J Inorg Mater, 2023, 38, 445 doi: 10.15541/jim20220767
[30]
Yang Z J, Wang L, Shi W, et al. Back to homogeneous computing: A tightly-coupled neuromorphic processor with neuromorphic ISA. IEEE Trans Parallel Distrib Syst, 2023, 34, 2910 doi: 10.1109/TPDS.2023.3307408
[31]
Sun B, Guo T, Zhou G D, et al. Synaptic devices based neuromorphic computing applications in artificial intelligence. Mater Today Phys, 2021, 18, 100393 doi: 10.1016/j.mtphys.2021.100393
[32]
Al-khamis K M, Mahfouz R M, Al-warthan A A, et al. Synthesis and characterization of gallium oxide nanoparticles. Arab J Chem, 2009, 2, 73 doi: 10.1016/j.arabjc.2009.10.001
[33]
Kang T X, Yang D M, Du F Q, et al. Using magnesium reduction strategy to produce black Ga2O3 with variable oxygen vacancies for photocatalytic applications. J Alloys Compd, 2022, 926, 166887 doi: 10.1016/j.jallcom.2022.166887
[34]
Wu C, Wu F, Ma C, et al. A general strategy to ultrasensitive Ga2O3 based self-powered solar-blind photodetectors. Mater Today Phys, 2022, 23, 100643 doi: 10.1016/j.mtphys.2022.100643
[35]
Jiang M, Zhang J Y, Yang W X, et al. Flexible self-powered photoelectrochemical photodetector with ultrahigh detectivity, ultraviolet/visible reject ratio, stability, and a quasi-invisible functionality based on lift-off vertical (Al, Ga)N nanowires. Adv Mater Interfaces, 2022, 9, 2200028 doi: 10.1002/admi.202200028
[36]
Zhang J Y, Jiao B, Dai J F, et al. Enhance the responsivity and response speed of self-powered ultraviolet photodetector by GaN/CsPbBr3 core-shell nanowire heterojunction and hydrogel. Nano Energy, 2022, 100, 107437 doi: 10.1016/j.nanoen.2022.107437
[37]
Das U, Sarkar P, Paul B, et al. Halide perovskite two-terminal analog memristor capable of photo-activated synaptic weight modulation for neuromorphic computing. Appl Phys Lett, 2021, 118, 182103 doi: 10.1063/5.0049161
[38]
Yuan S, Feng Z, Qiu B, et al. Silicon carbide nanowire-based multifunctional and efficient visual synaptic devices for wireless transmission and neural network computing. Sci China Mater, 2023, 66, 3238 doi: 10.1007/s40843-023-2472-0
[39]
Hofer S B, Mrsic-Flogel T D, Bonhoeffer T, et al. Experience leaves a lasting structural trace in cortical circuits. Nature, 2009, 457, 313 doi: 10.1038/nature07487
[40]
Yan X B, Wang J J, Zhao M L, et al. Artificial electronic synapse characteristics of a Ta/Ta2O5-x/Al2O3/InGaZnO4 memristor device on flexible stainless steel substrate. Appl Phys Lett, 2018, 113, 013503 doi: 10.1063/1.5027776
[41]
Zhang S, Yang L, Jiang C P, et al. Digitally aligned ZnO nanowire array based synaptic transistors with intrinsically controlled plasticity for short-term computation and long-term memory. Nanoscale, 2021, 13, 19190 doi: 10.1039/D1NR04156H
[42]
Liu G, Wang C, Zhang W B, et al. Organic biomimicking memristor for information storage and processing applications. Adv Electron Mater, 2016, 2, 1500298 doi: 10.1002/aelm.201500298
[43]
Qi H X, Wu Y. Synaptic plasticity of TiO2 nanowire transistor. Microelectron Int, 2020, 37, 125 doi: 10.1108/MI-08-2019-0053
[44]
Li R Z, Dong Y B, Qian F S, et al. CsPbBr3/graphene nanowall artificial optoelectronic synapses for controllable perceptual learning. PhotoniX, 2023, 4, 4 doi: 10.1186/s43074-023-00082-8
[45]
He K, Liu Y Q, Yu J C, et al. Artificial neural pathway based on a memristor synapse for optically mediated motion learning. ACS Nano, 2022, 16, 9691 doi: 10.1021/acsnano.2c03100
[46]
Kim J H, Lee H J, Kim H J, et al. Oxide semiconductor memristor-based optoelectronic synaptic devices with quaternary memory storage. Adv Electron Mater, 2024, 2300863 doi: 10.1002/aelm.202300863
[47]
Xie P S, Huang Y L, Wang W, et al. Ferroelectric P(VDF-TrFE) wrapped InGaAs nanowires for ultralow-power artificial synapses. Nano Energy, 2022, 91, 106654 doi: 10.1016/j.nanoen.2021.106654
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    Received: 24 May 2024 Revised: 27 June 2024 Online: Accepted Manuscript: 18 July 2024Uncorrected proof: 13 August 2024

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      Liubin Yang, Xiushuo Gu, Min Zhou, Jianya Zhang, Yonglin Huang, Yukun Zhao. Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing[J]. Journal of Semiconductors, 2024, In Press. doi: 10.1088/1674-4926/24050037 ****L B Yang, X S Gu, M Zhou, J Y Zhang, Y L Huang, and Y K Zhao, Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing[J]. J. Semicond., 2024, accepted doi: 10.1088/1674-4926/24050037
      Citation:
      Liubin Yang, Xiushuo Gu, Min Zhou, Jianya Zhang, Yonglin Huang, Yukun Zhao. Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing[J]. Journal of Semiconductors, 2024, In Press. doi: 10.1088/1674-4926/24050037 ****
      L B Yang, X S Gu, M Zhou, J Y Zhang, Y L Huang, and Y K Zhao, Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing[J]. J. Semicond., 2024, accepted doi: 10.1088/1674-4926/24050037

      Deep-UV-photo-excited synaptic Ga2O3 nano-device with low-energy consumption for neuromorphic computing

      DOI: 10.1088/1674-4926/24050037
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      • Liubin Yang is currently pursuing the master's degree in engineering from Nanjing University of Posts and Telecommunications. His research interests include semiconductor nanowires and devices
      • Yonglin Huang received the Ph.D. degree from Institute of Optics, Nankai University in 2003. He is a Professor and Master Supervisor at Nanjing University of Posts and Telecommunications. He has been engaged in teaching and research in optoelectronics and optical communication technology for more than 20 years. His main research fields are optical filters, optical add drop multiplexers, optical switches in optical wavelength division multiplexing systems and optical fiber grating sensing
      • Yukun Zhao is an Associate Research Fellow at Chinese Academy of Sciences and a Master Supervisor at University of Science and Technology of China. He received the double BS degrees in 2012 and PhD degree in 2017 at Xi'an Jiaotong University. He studied in University of Liverpool (UK) and Leibniz Association (Germany) for total 2 years supported by national official scholarship. Nowadays, he serves as the Youth Editorial Board Member for journals Chip (Elsevier), Exploration and Brain-X (Wiley), Guest Editor for 2 SCI journals by managing Special Issue, as well as the Reviewer for about 20 SCI journals, such as Nano Energy, ACS Appl Mater Interfaces, etc. He is also a reviewer for both national and provincial projects. His research interests include GaN-based nanowires, semiconductor devices and neuromorphic chips
      • Corresponding author: huangyl@njupt.edu.cnykzhao2017@sinano.ac.cn
      • Received Date: 2024-05-24
      • Revised Date: 2024-06-27
      • Available Online: 2024-07-18

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