Litcius/Paper detail

Understanding of the Volatile and Nonvolatile Switching in Ag-Based Memristors

Xiangxiang Ding, Peng Huang, Yudi Zhao, Yulin Feng, Lifeng Liu

2022IEEE Transactions on Electron Devices36 citationsDOI

Abstract

Memristors, possessing volatile and nonvolatile switching characteristics, exhibit great potential for high-density data storage and neuromorphic computing system. However, the adjustment of device electrical characteristics, such as drive current and threshold voltage, is still not specific, which hinders the optimization of memristors for desired applications. In this work, we fabricated Ag-based memristive devices with different stoichiometries and density of silicon suboxide (SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> ) dielectric layers, which can both exhibit volatile and nonvolatile switching behaviors and the volatile and nonvolatile switching can transfer each other by varying the compliance current. The experimental results showed that high stoichiometry of SiO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> film will lead to high compliance current for the transition from volatile to nonvolatile characteristics in Ag-based memristors. A model based on classical nucleation theory (CNT) was proposed to explain the transition current of different samples. It was found that the volatile and nonvolatile switching behaviors had a close correlation with the surface energy of conductive filament and defects in the dielectric layer. Besides, the dielectric layer of memristors deposited under low RF power exhibited large density, which caused large threshold voltage during the set process. This work provides the adjustment methods of drive current and threshold voltage from material properties.

Topics & Concepts

Non-volatile memoryMaterials scienceMemristorDielectricNeuromorphic engineeringOptoelectronicsNucleationThreshold voltageLayer (electronics)VoltageNanotechnologyElectrical engineeringTransistorChemistryComputer scienceArtificial neural networkMachine learningEngineeringOrganic chemistryAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering