Artificial synapse based on MoO<sub>3</sub> nanosheets prepared by hydrothermal synthesis

Guo Ke-Xin; Yu Hai-Yang; Han Hong; Wei Huan-Huan; Gong Jiang-Dong; Liu Lu; Huang Qian; Gao Qing-Yun; Xu Wen-Tao

doi:10.7498/aps.69.20200928

Abstract

Recently, neuromorphic systems capable of parallel information processing have attracted increasing attention. A neuromorphic system is desired to emulate a human brain, which consists of hundreds of billions of neurons connected with even more synapses. Synapses are important connection parts between neurons to transmit information through release and reception of neurotransmitters. A neuromorphic system could replicate brain learning, cognition and computation of a human brain to process huge data with 10¹⁶ floating point numbers per second. The high computing efficiency has attracted many researchers to study artificial synapses for application in future artificial intelligence. The synaptic weight could be adjusted by the received information. This provides a basis for the learning and computing capability of artificial synapses. So far, a number of semiconductor materials have been used in artificial synaptic devices, like some organic materials, e.g. Poly(3-hexylthiophene-2,5-diyl)(P3HT), [1]Benzothieno[3,2-b][1]benzothiophene, 2,7-dioctyl-(C8-BTBT) etc, some inorganic oxides such as zinc oxide, indium zinc oxide(IZO), indium gallium zinc oxide(IGZO), transition metal oxides, etc, and two-dimensional materials, e.g. graphene, black phosphorus, and organic-inorganic hybrid perovskite materials. Among them, transition metal oxides are attractive due to their unique layered structure and inherent properties, which are important in photohydrolysis, lithium ion batteries, and field-effect transistors. MoO₃, as a typical transition-metal oxide, has been used in artificial synaptic devices, with different preparation methods, such as mechanical exfoliation, chemical vapor deposition (CVD) and chemical vapor transportation (CVT), pulse-laser deposition (PLD). Here, we report the preparation of a semiconductor layer of MoO₃ nanosheets by hydrothermal method, and the use of a TiO₂ nanoparticle seed layer to improve the adhesion of MoO₃ nanosheets. This is a cost-effective and controllable process. The high surface-to-volume ratio of the material provides large contact area at the interface to allow easy ion diffusion. The device emulates important synaptic functions, such as excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), spike-duration dependent plasticity (SDDP), spike-voltage dependent plasticity (SVDP) and spike-rate dependent plasticity (SRDP). This work could be an important addition to the neuromorphic research field.

Keywords:

Authors and contacts

Key Laboratory of Photoelectronic Thin Film Devices and Technology of Tianjin, Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Tianjin 300350, China

Corresponding author: Xu Wen-Tao, wentao@nankai.edu.cn

Funds: Project supported by the Key Area R&D Program of Guangdong Province, China (Grant No. 2018B030338001), the Tianjin Science Foundation for Distinguished Young Scholars, China (Grant No. 19JCJQJC61000), the Natural Science Foundation of Tianjin, China (Grant No. 18JCYBJC16000), and the Fundamental Research Funds for the Central Universities, China (Grant Nos. 075-63191740, 075-63191745)

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References

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图 1 (a) 生物神经元及神经突触示意图; (b) 人工突触器件结构示意图; (c) MoO₃结构示意图

Figure 1. (a) Schematic diagram of biological neurons and synapse; (b) schematic diagram of artificial synapse device; (c) schematic diagram of MoO₃ structure.

DownLoad: Full-Size Img PowerPoint

图 2 (a) MoO₃纳米材料XRD测试图; (b) MoO₃纳米材料扫描电镜图; (c) MoO₃纳米材料的N₂等温吸脱附曲线

Figure 2. (a) XRD of MoO₃ nanomaterials; (b) scanning electron microscope image of MoO₃ nanomaterials; (c) nitrogen Isothermal absorption and desorption curve of MoO₃ nanomaterials.

DownLoad: Full-Size Img PowerPoint

图 3 (a) 单个幅值为1 V的刺激在MoO₃人工神经突触上引起的EPSC; (b)一对幅值为1 V的刺激在MoO₃人工神经突触上引起的PPF; (c)和(d) 多对时间间隔不同, 幅值为1 V的脉冲引起的PPF及PPF Index

Figure 3. (a) EPSC triggered by a single 1 V spike at a MoO₃ artificial synapse; (b) PPF triggered by a pair of 1 V spikes at a MoO₃ artificial synapse; (c) and (d) PPF and PPF index triggered by spikes with different time intervals and same amplitudes of 1 V.

DownLoad: Full-Size Img PowerPoint

图 4 (a) 施加幅值为1 V刺激个数分别为1, 2, 3, 5, 8在MoO₃突触器件上引起的的SRDP; (b) 相同幅值不同刺激持续时间造成的SDDP; (c) 幅值分别为0.5, 1.0, 1.5, 2.0 V的刺激在突触器件上引起的SVDP; (d)为施加幅值为0.2 V时所获得的兴奋性突触后电流及完成一次信号传递所消耗的能量

Figure 4. (a) SRDP on MoO₃ synapses triggered by the number of spikes of 1 V applied at 1, 2, 3, 5, and 8; (b) SDDP triggered by different spike duration time with the same amplitude; (c) SVDP triggered by spikes with amplitudes of 0.5, 1.0, 1.5 V, and 2.0 V on synaptic devices; (d) the excitatory postsynaptic current when the applied spike is 0.2 V and the energy consumed to complete a signal transmission.

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表 1 不同金属氧化物人工突触器件能量消耗

Table 1. Energy consumption of different metal oxide artificial synaptic devices.

序号	活性层	制备方法	能量消耗/pJ	文献
1	MoO₃	水热法	1.47	本文
2	MoO₃	化学气相沉积	9.60	[30]
3	HfO_x/AlO_x	溶液法	6.00	[44]
4	Pt/HfO_x/TiO_y/HfO_x/ TiO_y/TiN	磁控溅射	0.30	[45]
5	HfO_x/CeO_x	等离子体辅助原子层沉积 (plasma-assisted atomic layer deposition)	~8.00	[46]
6	ZnSnO	静电纺丝	0.44	[47]
7	IZO/SiO₂	射频磁控溅射	45.00	[48]
8	IZO/GO	滴涂	3.70	[16]

DownLoad: CSV

[1]	王洋昊, 刘昌, 黄如, 杨玉超 2020 科学通报 65 46 Wang Y H, Liu C, Huang R, Yang Y C 2020 Chin. Sci. Bull. 65 46
[2]	姜珊珊, 聂莎, 何勇礼, 刘锐, 万青 2019 现代电子技术 42 181 Jiang S S, Nie S, He Y L, Liu R, Wan Q 2019 Modern Electronic Technology 42 181
[3]	Yao Y, Huang X, Peng S, Zhang D, Shi J, Yu G, Liu Q, Jin Z 2019 Adv. Electron. Mater. 5 1800887 Google Scholar
[4]	Kim S G, Kim S H, Park J, Kim G S, Park J H, Saraswat K C, Kim J, Yu H Y 2019 ACS Nano 13 10294 Google Scholar
[5]	Shi C, Wang J, Sushko M L, Qiu W, Yan X, Liu X Y 2019 Adv. Funct. Mater. 29 1904777 Google Scholar
[6]	Kandel E R 2001 Science 294 1030 Google Scholar
[7]	Yang S N, Tang Y G, Zucker R S 1999 J. Neurophysiol. 81 781 Google Scholar
[8]	Shouval H Z, Bear M F, Cooper L N 2002 Proc. Natl. Acad. Sci. 99 10831 Google Scholar
[9]	Fang L, Dai S, Zhao Y, Liu D, Huang J 2020 Adv. Electron. Mater. 6 1901217 Google Scholar
[10]	Fuller E J, Keene S T, Melianas A, Wang Z, Agarwal S, Li Y, Tuchman Y, Jame C D, Marinella M J, Yang J J, Salleo A 2019 Science 364 570 Google Scholar
[11]	Lee Y, Lee T W 2019 Accounts Chem. Res. 52 964 Google Scholar
[12]	Wan C, Chen G, Fu Y, Wang M, Matsuhisa N, Pan S, Pan L, Yang H, Wan Q, Zhu L, Chen X 2018 Adv. Mater. 30 1801291 Google Scholar
[13]	Han H, Xu Z, Guo K, Ni Y, Ma M, Yu H, Wei H, Gong J, Zhang S, Xu W 2020 Adv. Intell. Syst. 2 1900176 Google Scholar
[14]	Balakrishna Pillai P, De Souza M M 2017 ACS Appl. Mater. Interfaces 9 1609 Google Scholar
[15]	Wan C J, Liu Y H, Zhu L Q, Feng P, Shi Y, Wan Q 2016 ACS Appl. Mater. Interfaces 8 9762 Google Scholar
[16]	Yang Y, Wen J, Guo L, Wan X, Du P, Feng P, Wan Q 2016 ACS Appl. Mater. Interfaces 8 30281 Google Scholar
[17]	Sun J, Oh S, Choi Y, Seo S, Oh M J, Lee M, Lee W B, Yoo P J, Cho J H, Park J H 2018 Adv. Funct. Mater. 28 1804397 Google Scholar
[18]	Yan X, Zhao J, Liu S, Zhou Z, Liu, Q, Chen J, Liu X Y 2018 Adv. Funct. Mater. 28 1705320 Google Scholar
[19]	Murase S, Yang Y 2012 Adv. Mater. 24 2459 Google Scholar
[20]	Yang Y, Brenner K, Murali R 2012 Carbon 50 1727 Google Scholar
[21]	Schedin F, Lidorikis E, Lombardo A, Kravets V G, Geim A K, Grigorenko A N, Novoselov K S, Ferrari A C 2010 ACS Nano 4 5617 Google Scholar
[22]	Li X, Zhu H, Wang K, Cao A, Wei J, Li C, Jia Y, Li Z, Li X, Wu D 2010 Adv. Mater. 22 2743 Google Scholar
[23]	Tian H, Guo Q, Xie Y, Zhao H, Li C, Cha J J, Xia F, Wang H 2016 Adv. Mater. 28 4991 Google Scholar
[24]	Kim S I, Lee Y, Park M H, Go G T, Kim Y H, Xu W, Lee H D, Kim H, Seo D G, Lee W, Lee T W 2019 Adv. Electron. Mater. 5 1900008 Google Scholar
[25]	Li M., Cui Z, Li E 2019 Ceram. Int. 45 14449 Google Scholar
[26]	Ji W, Shen R, Yang R, Yu G, Guo X, Peng L, Ding W 2014 J. Mater. Chem. A 2 699 Google Scholar
[27]	Wu F, Tian J, Su Y, Guan Y, Jin Y, Wang Z, He T, Bao L, Chen S 2014 J. Power Sources 269 747 Google Scholar
[28]	Liu E, Fu Y, Wang Y, et al. 2015 Nat. Commun. 6 1 Google Scholar
[29]	Li H, Wu J, Yin Z, Zhang H 2014 Accounts Chem. Res. 47 1067 Google Scholar
[30]	Yang C S, Shang D S, Chai Y S, Yan L Q, Shen B G, Sun Y 2017 Phys. Chem. Chem. Phys. 19 4190 Google Scholar
[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 2018 Adv. Funct. Mater. 28 1804170 Google Scholar
[32]	Wang Z, Yang R, Huang H M, He H K, Shaibo J, Guo X 2020 Adv. Electron. Mater. 6 1901290 Google Scholar
[33]	于焕芹 2018 硕士学位论文 (山东: 济南大学) Yu H Q 2018 M. S. Thesis (Shandong: Jinan University) (in Chinese)
[34]	Galatsis K, Li Y X, Wlodarski W, Comini E, Faglia G, Sberveglieri G 2001 Sens. Actuators B Chem. 77 472 Google Scholar
[35]	Liu Y, Yang S, Lu Y, Chen W, Zakharova G S 2015 Appl. Surf. Sci. 359 114 Google Scholar
[36]	徐英明, 王敏, 程晓丽, 霍丽华, 张现发 2016 中国专利 CN 105439202A Xu Y M, Wang M, Cheng X L, Huo L H, Zhang X F 2016 CN Patent CN 105439202A (in Chinese)
[37]	Zhang C, Chen Y, Yi M. Zhu Y, Li T, Liu L, Wang L, Xie L, Huang W 2018 Scientia Sinica Informationis 48 115 Google Scholar
[38]	Zucker R S, Regehr W G 2002 Annu. Rev. Physiol. 64 355 Google Scholar
[39]	Jiang J, Hu W, Xie D, Yang J, He J, Gao Y, Wan Q 2019 Nanoscale 11 1360 Google Scholar
[40]	Jo S H, Chang T, Ebong I, Bhadviya B B, Mazumder P, Lu W 2010 Nano Lett. 10 1297 Google Scholar
[41]	Yang C S, Shang D S, Liu N, Shi G, Shen X, Yu R C, L Y Q, Sun Y 2017 Adv. Mater. 29 1700906 Google Scholar
[42]	Bi G Q, Poo M M 2001 Annu. Rev. Neurosci. 24 139 Google Scholar
[43]	Yu H Y, Gong J D, Wei H H, Huang W, Xu W T 2019 Mater. Chem. Front. 3 941 Google Scholar
[44]	Yu S, Wu Y, Jeyasingh R, Kuzum D, Wong P H S 2011 IEEE Trans Electron Devices 58 2729 Google Scholar
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[46]	Hsieh C C, Roy A, Chang Y F, Shahrjerdi D, Banerjee S K 2016 Appl. Phys. Lett. 109 223501 Google Scholar
[47]	Zhu Y, Shin B, Liu G, Shan F 2019 IEEE Electron Device Lett. 40 1776 Google Scholar
[48]	Zhu L Q, Wan C J, Guo L Q, Shi Y, Wan Q 2014 Nat. Commun. 5 1

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Artificial synapse based on MoO₃ nanosheets prepared by hydrothermal synthesis

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Corresponding author: Xu Wen-Tao, wentao@nankai.edu.cn

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Artificial synapse based on MoO3 nanosheets prepared by hydrothermal synthesis

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Corresponding author: Xu Wen-Tao, wentao@nankai.edu.cn

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Artificial synapse based on MoO₃ nanosheets prepared by hydrothermal synthesis