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

Volume 2 Issue 3

August 2023

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Fang Nie, Jie Wang, Hong Fang, Shuanger Ma, Feiyang Wu, Wenbo Zhao, Shizhan Wei, Yuling Wang, Le Zhao, Shishen Yan, Chen Ge, Limei Zheng. Ultrathin SrTiO3-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing[J]. Materials Futures, 2023, 2(3): 035302. doi: 10.1088/2752-5724/ace3dc

Citation:

Fang Nie, Jie Wang, Hong Fang, Shuanger Ma, Feiyang Wu, Wenbo Zhao, Shizhan Wei, Yuling Wang, Le Zhao, Shishen Yan, Chen Ge, Limei Zheng. Ultrathin SrTiO₃-based oxide memristor with both drift and diffusive dynamics as versatile synaptic emulators for neuromorphic computing[J]. Materials Futures, 2023, 2(3): 035302. 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

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, ,

Materials Futures, Volume 2, Number 3

Received Date: 2023-05-26
Accepted Date: 2023-07-04
Publish Date: 2023-07-26

Article information

doi: 10.1088/2752-5724/ace3dc

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).

Abstract

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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References(25)

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