Ultrathin SrTiO<sub>3</sub>-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing

Fang Nie; Jie Wang; Hong Fang; Shuanger Ma; Feiyang Wu; Wenbo Zhao; Shizhan Wei; Yuling Wang; Le Zhao; Shishen Yan; Chen Ge; Limei Zheng

doi:10.1088/2752-5724/ace3dc

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Ultrathin SrTiO₃-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing

doi: 10.1088/2752-5724/ace3dc

Fang Nie^1,2,7,
Jie Wang^3,4,7,
Hong Fang^3,4,7,
Shuanger Ma¹,
Feiyang Wu¹,
Wenbo Zhao¹,
Shizhan Wei¹,
Yuling Wang⁵,
Le Zhao^{2
, ,},
Shishen Yan^1,4,
Chen Ge^{6
, ,},
Limei Zheng^{1
, ,}

1 School of Physics, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, People’s Republic of China
2 School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, People’s Republic of China
3 Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, People’s Republic of China
4 Spintronics Institute, School of Physics and Technology, University of Jinan, Jinan 250022, People’s Republic of China
5 Heilongjiang Provincial Key Laboratory of Oilfield Applied Chemistry and Technology, School of Mechatronic Engineering, Daqing Normal University, Daqing 163712, People’s Republic of China
6 Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China

Funds:

The authors acknowledge the support from the National Key Research & Development Program of China (No. 2021YFB3601504), the National Natural Science Foundation of China (Nos. 52072218, 12222414, 12074416), the Natural Science Foundation of Shandong province (Nos. ZR2022YQ43 and ZR2020ZD28), Heilongjiang Provincial Natural Resources Foundation Joint Guide Project (No. LH2020E098), and Peixin Fund of Qilu University of Technology (Shandong Academy of Sciences) (No. 2023PY093).

摘要

摘要:
Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here, we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO₃/Nb: SrTiO₃ heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity (STP/LTP), transition from STP to LTP, learning–forgetting–relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give rise to the coexistence of volatile- and nonvolatile-type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.
- memristor /
- artificial synapse /
- synaptic plasticity /
- associative learning /
- learning-experience
Abstract:
Artificial synapses are electronic devices that simulate important functions of biological synapses, and therefore are the basic components of artificial neural morphological networks for brain-like computing. One of the most important objectives for developing artificial synapses is to simulate the characteristics of biological synapses as much as possible, especially their self-adaptive ability to external stimuli. Here, we have successfully developed an artificial synapse with multiple synaptic functions and highly adaptive characteristics based on a simple SrTiO₃/Nb: SrTiO₃ heterojunction type memristor. Diverse functions of synaptic learning, such as short-term/long-term plasticity (STP/LTP), transition from STP to LTP, learning–forgetting–relearning behaviors, associative learning and dynamic filtering, are all bio-realistically implemented in a single device. The remarkable synaptic performance is attributed to the fascinating inherent dynamics of oxygen vacancy drift and diffusion, which give rise to the coexistence of volatile- and nonvolatile-type resistive switching. This work reports a multi-functional synaptic emulator with advanced computing capability based on a simple heterostructure, showing great application potential for a compact and low-power neuromorphic computing system.
- memristor /
- artificial synapse /
- synaptic plasticity /
- associative learning /
- learning-experience

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参考文献(25)

[1]	Wang J R and Zhuge F 2019 Memristive synapses for brain-inspired computing Adv. Mater. Technol. 4 1800544
[2]	Xi F B, Han Y, Liu M S, Bae J H, Tiedemann A, Gru¨tzmacher D and Zhao Q T 2021 Artificial synapses based on ferroelectric Schottky barrier field-effect transistors for neuromorphic applications ACS Appl. Mater. Interfaces 13 32005–12
[3]	Zhang H Z, Ju X, Yew K S and Ang D S 2020 Implementation of simple but powerful trilayer oxide-based artificial synapses with a tailored bio-synapse-like structure ACS Appl. Mater. Interfaces 12 1036–45
[4]	Pereda A E 2014 Electrical synapses and their functional interactions with chemical synapses Nat. Rev. Neurosci. 15 250–63
[5]	Chang T, Jo S H and Lu W 2011 Short-term memory to long-term memory transition in a nanoscale memristor ACS Nano 5 7669–76
[6]	Yang S T et al 2022 High-performance neuromorphic computing based on ferroelectric synapses with excellent conductance linearity and symmetry Adv. Funct. Mater. 32 2202366
[7]	Kuzum D, Jeyasingh R G D, Lee B and Wong H S P 2012 Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing Nano Lett. 12 2179–86
[8]	Sokolov A S, Jeon Y R, Kim S, Ku B and Choi C 2019 Bio-realistic synaptic characteristics in the cone-shaped ZnO memristive device NPG Asia Mater. 11 1–15
[9]	Ohno T, Hasegawa T, Tsuruoka T, Terabe K, Gimzewski J K and Aono M 2011 Short-term plasticity and long-term potentiation mimicked in single inorganic synapses Nat. Mater. 10 591–5
[10]	Nayak A, Ohno T, Tsuruoka T, Terabe K, Hasegawa T, Gimzewski J K and Aono M 2012 Controlling the synaptic plasticity of a Cu₂S gap-type atomic switch Adv. Funct. Mater. 22 3606–13
[11]	Li J K, Ge C, Du J Y, Wang C, Yang G Z and Jin K J 2020 Reproducible ultrathin ferroelectric domain switching for high-performance neuromorphic computing Adv. Mater. 32 1905764
[12]	Yang Y, Wen J, Guo L Q, Wan X, Du P F, Feng P, Shi Y and Wan Q 2016 Long-term synaptic plasticity emulated in modified graphene oxide electrolyte gated IZO-based thin-film transistors ACS Appl. Mater. Interfaces 8 30281–6
[13]	John R A et al 2022 Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing Nat. Commun. 13 2074
[14]	Wang Z R et al 2017 Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing Nat. Mater. 16 101–8
[15]	Midya R et al 2019 Artificial neural network (ANN) to spiking neural network (SNN) converters based on diffusive memristors Adv. Electron. Mater. 5 1900060
[16]	Li J K, Li N, Ge C, Huang H Y, Sun Y W, Gao P, He M, Wang C, Yang G Z and Jin K J 2019 Giant electroresistance in ferroionic tunnel junctions iScience 16 368–77
[17]	Yang R, Huang H M and Guo X 2019 Memristive synapses and neurons for bioinspired computing Adv. Electron. Mater. 5 1900287
[18]	Liu G, Wang C, Zhang W B, Pan L, Zhang C C, Yang X, Fan F, Chen Y and Li R W 2016 Organic biomimicking memristor for information storage and processing applications Adv. Electron. Mater. 2 1500298
[19]	Yang J T, Ge C, Du J Y, Huang H Y, He M, Wang C, Lu H B, Yang G Z and Jin K J 2018 Artificial synapses emulated by an electrolyte-gated tungsten-oxide transistor Adv. Mater. 30 1801548
[20]	Liu Y H, Zhu L Q, Feng P, Shi Y and Wan Q 2015 Freestanding artificial synapses based on laterally proton-coupled transistors on chitosan membranes Adv. Mater. 27 5599–604
[21]	Shen Z H, Li W H, Tang X G, Hu J, Wang K Y, Jiang Y P and Guo X B 2022 An artificial synapse based on Sr(Ti, Co)O3 films Mater. Today Commun. 33 104754
[22]	Ren Z Y, Zhu L Q, Guo Y B, Long T Y, Yu F, Xiao H and Lu H L 2020 Threshold tunable spike rate dependent plasticity originated from interfacial proton gating for pattern learning and memory ACS Appl. Mater. Interfaces 12 7833–9
[23]	Yin L, Huang W, Xiao R L, Peng W B, Zhu Y Y, Zhang Y Q, Pi X D and Yang D 2020 Optically stimulated synaptic devices based on the hybrid structure of silicon nanomembrane and perovskite Nano Lett. 20 3378–87
[24]	Zhao L et al 2020 An artificial optoelectronic synapse based on a photoelectric memcapacitor Adv. Electron. Mater. 6 1900858
[25]	Lao J, Xu W, Jiang C L, Zhong N, Tian B B, Lin H C, Luo C H, Sejdic J T, Peng H and Duan C G 2021 Artificial synapse based on organic-inorganic hybrid perovskite with electric and optical modulation Adv. Electron. Mater. 7 2100291