The elusive memristor: properties of basic electrical circuits

By Y. Joglekar, Stephen J. Wolf. 2008

904 citations recorded by the source corpus.

We present an introduction to and a tutorial on the properties of the recently discovered ideal circuit element, a memristor. By definition, a memristor M relates the charge q and the magnetic flux ϕ in a circuit and complements a resistor R, a capacitor C and an inductor L as an ingredient of ideal electrical circuits. The properties of these three elements and their circuits are a part of the standard curricula. The existence of the memristor as the fourth ideal circuit element was predicted in 1971 based on symmetry arguments, but was clearly experimentally demonstrated just last year. We present the properties of a single memristor, memristors in series and parallel, as well as ideal memristor–capacitor (MC), memristor–inductor (ML) and memristor–capacitor–inductor (MCL) circuits. We find that the memristor has hysteretic current–voltage characteristics. We show that the ideal MC (ML) circuit undergoes non-exponential charge (current) decay with two time scales and that by switching the polarity of the capacitor, an ideal MCL circuit can be tuned from overdamped to underdamped. We present simple models which show that these unusual properties are closely related to the memristor's internal dynamics. This tutorial complements the pedagogy of ideal circuit elements (R, C and L) and the properties of their circuits, and is aimed at undergraduate physics and electrical engineering students.

Subfields: Electrical and Electronic Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/96eadcc36e26e4f14ad5c0ddb739ab5ed1697ec0

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Citations in the corpus

A Versatile Memristor Model With Nonlinear Dopant Kinetics (2011, methodology): “with p as a positive exponent parameter [18].”
SPICE Model of Memristor with Nonlinear Dopant Drift (2009, methodology): “Keywords Memristor, drift, window function, SPICE.”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, background): “6 For current/charge driven memristors, the boundary constraint was modeled by adding a multiplicative windowing function to allow for non-linear drift (Biolek et al., 2009; Joglekar and Wolf, 2009).”
A Memristor Device Model (2011, methodology): “This idea was theorized in [7] and [8] and determined experimentally in [10].”
Practical Approach to Programmable Analog Circuits With Memristors (2009, methodology): “To interpret the experimental observations and predict a circuit behaviour, a number of theoretical models were developed [2], [3], [21], [22], [1], [23], [24], [25], [26], [27], [28], [29] including SPICE models [27], [28], [29].”
Memristor Bridge Synapse-Based Neural Network and Its Learning (2012, methodology): “One model of the memristor which accounts for the nonlinear drift is the window model [32] The nonlinearity of the window function is inversely proportional to the parameter p.”
A Memristor-Based Hyperchaotic System with Hidden Attractors: Dynamics, Synchronization and Circuital Emulating (2015, background): “…such as secure communications, random generators, signal processing, pattern formation of modelling of complex systems [6-12], studies on memristor [13-21] have only received significant attention recently after the realization of a solid-state thin film two-terminal memristor at Hewlett-Packard…”
Dynamical Properties and Design Analysis for Nonvolatile Memristor Memories (2011, background): “Memristors can be charge controlled or flux controlled depending on the biasing condition [50]–[52].”
On the mathematical modeling of memristors (2010, methodology): “Recently the Memristor was modeled using a linearized model of the pinched i-v hysteresis as in [7] or qualitatively as in [8], [9].”
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit (2012, methodology): “Several different types of nonlinear memristor models with different window functions [16] have been proposed.”