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Chin. Phys. B, 2023, Vol. 32(1): 017304    DOI: 10.1088/1674-1056/ac673f

High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors

Xiao-Juan Lian(连晓娟)1,2, Jin-Ke Fu(付金科)1, Zhi-Xuan Gao(高志瑄)1, Shi-Pu Gu(顾世浦)1, and Lei Wang(王磊)1,†
1 The College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;
2 The National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Abstract  Threshold switching (TS) memristors can be used as artificial neurons in neuromorphic systems due to their continuous conductance modulation, scalable and energy-efficient properties. In this paper, we propose a low power artificial neuron based on the Ag/MXene/GST/Pt device with excellent TS characteristics, including a low set voltage (0.38 V) and current (200 nA), an extremely steep slope (< 0.1 mV/dec), and a relatively large off/on ratio (> 103). Besides, the characteristics of integrate and fire neurons that are indispensable for spiking neural networks have been experimentally demonstrated. Finally, its memristive mechanism is interpreted through the first-principles calculation depending on the electrochemical metallization effect.
Keywords:  memristors      artificial neurons      2D MXene      Ge2Sb2Te5  
Received:  16 January 2022      Revised:  02 April 2022      Accepted manuscript online:  14 April 2022
PACS:  73.40.-c (Electronic transport in interface structures)  
  83.10.Tv (Structural and phase changes)  
  85.35.-p (Nanoelectronic devices)  
  87.19.lj (Neuronal network dynamics)  
Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 61804079 and 61964012), the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology (Grant No. KFJJ20200102), the Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20211273 and BZ2021031), the Nanjing University of Posts and Telecommunications (Grant No. NY220112), and the Foundation of Jiangxi Science and Technology Department (Grant No. 20202ACBL21200).
Corresponding Authors:  Lei Wang     E-mail:

Cite this article: 

Xiao-Juan Lian(连晓娟), Jin-Ke Fu(付金科), Zhi-Xuan Gao(高志瑄),Shi-Pu Gu(顾世浦), and Lei Wang(王磊) High-performance artificial neurons based on Ag/MXene/GST/Pt threshold switching memristors 2023 Chin. Phys. B 32 017304

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