Tuning Resistive Switching Behavior by Controlling Internal Ionic Dynamics for Biorealistic Implementation of Synaptic Plasticity

Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics...

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Bibliographic Details
Published in:Advanced electronic materials 2022-08, Vol.8 (8), p.n/a
Main Authors: Yoo, Sangmin, Wu, Yuting, Park, Yongmo, Lu, Wei D.
Format: Article
Language:English
Subjects:
2nd order memristors
correlation detection
neuromorphic computing
spike‐timing dependent plasticity
spiking neural network (SNN)
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Summary:Memristive devices have demonstrated rich switching behaviors that closely resemble synaptic functions and provide a building block to construct efficient neuromorphic systems. It is demonstrated that resistive switching effects are controlled not only by the external field, but also by the dynamics of various internal state variables that facilitate the ionic processes. The internal temperature, for example, works as a second‐state variable to regulate the ion motion and provides the internal timing mechanism for the native implementation of timing‐ and rate‐based learning rules such as spike timing dependent plasticity (STDP). In this work, it is shown that the 2nd state‐variable in a Ta2O5‐based memristor, its internal temperature, can be systematically engineered by adjusting the material properties and device structure, leading to tunable STDP characteristics with different time constants. When combined with an artificial post‐synaptic neuron, the 2nd‐order memristor synapses can spontaneously capture the temporal correlation in the input streaming events. By tuning material properties and device structures, the internal dynamics of a 2nd‐order memristor can be tailored, allowing it to natively exhibit timing‐based learning rules such as spike‐timing dependent plasticity with adjustable time constants. These memristors can be used to form bio‐realistic networks and naturally process temporal data such as detecting correlation patterns in input spike trains.
ISSN:2199-160X
2199-160X
DOI:10.1002/aelm.202101025