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

Xiaojuan Lian, Jinke Fu, Zhixuan Gao, Shi‐Pu Gu, Lei Wang

2022Chinese Physics B19 citationsDOI

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 (> 10 3 ). 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.

Topics & Concepts

MemristorNeuromorphic engineeringScalabilityMaterials scienceConductanceVoltageModulation (music)Power (physics)Artificial neural networkOptoelectronicsComputer scienceThreshold voltageArtificial neuronEnergy (signal processing)Set (abstract data type)Topology (electrical circuits)Biological systemElectronic engineeringElectrical engineeringArtificial intelligenceTransistorPhysicsCondensed matter physicsAcousticsDatabaseBiologyProgramming languageEngineeringQuantum mechanicsAdvanced Memory and Neural ComputingMXene and MAX Phase MaterialsFerroelectric and Negative Capacitance Devices