Circuit Elements With Memory: Memristors, Memcapacitors, and Meminductors

By M. Di Ventra, Yuriy V. Pershin, Leon Ong Chua. Proceedings of the IEEE, 2009

1,040 citations recorded by the source corpus.

We extend the notion of memristive systems to capacitive and inductive elements, namely, capacitors and inductors whose properties depend on the state and history of the system. All these elements typically show pinched hysteretic loops in the two constitutive variables that define them: current-voltage for the memristor, charge-voltage for the memcapacitor, and current-flux for the meminductor. We argue that these devices are common at the nanoscale, where the dynamical properties of electrons and ions are likely to depend on the history of the system, at least within certain time scales. These elements and their combination in circuits open up new functionalities in electronics and are likely to find applications in neuromorphic devices to simulate learning, adaptive, and spontaneous behavior.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0cf365e12ff39b82aeaaeabfb1b9bbc01d620eca

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Citations in the corpus

Memristor-based neural networks (2013, methodology): “We follow the deﬁnitions provided by Di Ventra et al [2, 3].”
Integration of nanoscale memristor synapses in neuromorphic computing architectures (2013, background): “biomimetic applications presented at the same time. The functional signature of memristors is a pinched hysteresis loop in the current-voltage (i-v) domain when excited by a bipolar periodic stimulus [31]. Such hysteresis is typically noticed for all kind of devices/materials in support of a dischar”
Practical Approach to Programmable Analog Circuits With Memristors (2009, methodology): “Our scheme represents an important application of a new class of emerging systems collectively called memorycircuit elements ( memelements ) [22].”
On the mathematical modeling of memristors (2010, methodology): “The invention of the Memristor was the key for postulating new elements by Chua such as the Memcapacitors and the Meminductors [3].”
The Fourth Element (2012, background): “called memcapacitor, and meminductor respectively [11], and are identified by their corresponding symbols.”
Memristor Crossbar-Based Neuromorphic Computing System: A Case Study (2014, background): “Intrinsically, a memristor behaves similarly to a synapse: it can remember the total electric charge/flux ever to flow through it [8], [9].”
Analogue signal and image processing with large memristor crossbars (2017, methodology): “Many of these operations can be expressed as a vector-matrix multiplication (VMM), which in principle can be performed in the analogue domain by a memristor crossbar array 5–10 using Ohm’s law for multiplication and Kirchhoff’s current law for summation 11–30 (Fig.”