Memory effects in complex materials and nanoscale systems

By Yuriy V. Pershin, M. Di Ventra. 2010

738 citations recorded by the source corpus.

Memory effects are ubiquitous in nature and are particularly relevant at the nanoscale where the dynamical properties of electrons and ions strongly depend on the history of the system, at least within certain time scales. We review here the memory properties of various materials and systems which appear most strikingly in their non-trivial, time-dependent resistive, capacitative and inductive characteristics. We describe these characteristics within the framework of memristors, memcapacitors and meminductors, namely memory-circuit elements with properties that depend on the history and state of the system. We examine basic issues related to such systems and critically report on both theoretical and experimental progress in understanding their functionalities. We also discuss possible applications of memory effects in various areas of science and technology ranging from digital to analog electronics, biologically inspired circuits and learning. We finally discuss future research opportunities in the field.

Subfields: Electrical and Electronic Engineering, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/ca16bf06c334796f52dd961191addd71a4b49747

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In Electrical and Electronic Engineering

In Polymers and Plastics

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Citations in the corpus

Multi-terminal memtransistors from polycrystalline monolayer molybdenum disulfide (2018, background): “Since ID normally scales as W/L, the observed deviation in this scaling at breakdown suggests that the reverse-biased Schottky diode at the source electrode creates a bottleneck for electron injection at the channel,(27) ultimately causing electromigration at the source contact.(7) This reversible b”
STDP and STDP variations with memristors for spiking neuromorphic learning systems (2013, background): “Zamarreño et al. showed extensive simulations on these concepts, and how one can change from STDP to anti-STDP by switching polarities of spikes or memristors (Zamarreño-Ramos et al., 2011).”
Three Fingerprints of Memristor (2013, background): “For the examples presented in [13] it was noted that non-transversal loops occurred when and are even functions of , where , but this was not a necessary condition as transversal pinched hysteresis loops were observed in unipolar resistance switching devices where and were even functions of .”
Memristor-based neural networks (2013, background): “The result is a characteristic, pinched hysteresis curve. an even further generalization of equation (1), please refer to Pershin and Di Ventra [5].”
Experimental photonic quantum memristor (2021, background): “implementation and results The input state proposed in equation (5) encodes a qubit as a superposition of two energy levels.”