Redox memristors with volatile threshold switching behavior for neuromorphic computing

The spiking neural network (SNN), closely inspired by the human brain, is one of the most powerful platforms to enable highly efficient, low cost, and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system. In the hardware implementation, the building of artificial spiking neurons is fundamental for constructing the whole system. However, with the slowing down of Moore’s Law, the traditional complementary metal-oxide-semiconductor (CMOS) technology is gradually fading and is unable to meet the growing needs of neuromorphic computing. Besides, the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices. Memristors with volatile threshold switching (TS) behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems. Herein, the state-of-the-art about the fundamental knowledge of SNNs is reviewed. Moreover, we review the implementation of TS memristor-based neurons and their systems, and point out the challenges that should be further considered from devices to circuits in the system demonstrations. We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors

Enhancing Reliability of Studies on Single Filament Memristive Switching via an Unconventional cAFM Approach

Memristive devices are highly promising for implementing neuromorphic functionalities in future electronic hardware, and direct insights into memristive phenomena on the nanoscale are of fundamental importance to reaching this. Conductive atomic force microscopy (cAFM) has proven to be an essential tool for probing memristive action locally on the nanoscale, but the significance of the acquired data frequently suffers from the nonlocality associated with the thermal drift of the tip in ambient conditions. Furthermore, comparative studies of different configurations of filamentary devices have proven to be difficult, because of an immanent variability of the filament properties between different devices. Herein, these problems are addressed by constraining the memristive action directly at the apex of the probe through functionalization of a cAFM tip with an archetypical memristive stack, which is comprised of Ag/Si3N4. The design of such functionalized cantilevers (entitled here as "memtips") allowed the capture of the long-term intrinsic current response, identifying temporal correlations between switching events, and observing emerging spiking dynamics directly at the nanoscale. Utilization of an identical memtip for measurements on different counter electrodes made it possible to directly compare the impact of different device configurations on the switching behavior of the same filament. Such an analytical approach in ambient conditions will pave the way towards a deeper understanding of filamentary switching phenomena on the nanoscale

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Physical reservoir computing (RC) represents a computational framework that exploits information-processing capabilities of programmable matter, allowing the realization of energy-efficient neuromorphic hardware with fast learning and low training cost. Despite self-organized memristive networks have been demonstrated as physical reservoir able to extract relevant features from spatiotemporal input signals, multiterminal nanonetworks open the possibility for novel strategies of computing implementation. In this work, we report on implementation strategies of in materia RC with self-assembled memristive networks. Besides showing the spatiotemporal information processing capabilities of self-organized nanowire networks, we show through simulations that the emergent collective dynamics allows unconventional implementations of RC where the same electrodes can be used as both reservoir inputs and outputs. By comparing different implementation strategies on a digit recognition task, simulations show that the unconventional implementation allows a reduction of the hardware complexity without limiting computing capabilities, thus providing new insights for taking full advantage of in materia computing toward a rational design of neuromorphic systems

Modeling of Short-Term Synaptic Plasticity Effects in ZnO Nanowire-Based Memristors Using a Potentiation-Depression Rate Balance Equation

This letter deals with short-term plasticity (STP) effects in the conduction characteristics of single crystalline ZnO nanowires, including potentiation, depression and relaxation phenomena. The electrical behavior of the structures is modeled following Chua's approach for memristive systems, i.e. one equation for the electron transport and one equation for the memory state of the device. Linear conduction is assumed in the first case together with a voltage-controlled rate balance equation for the normalized conductance. The devices are subject to electrical stimuli such as ramped and pulsed voltages of both polarities with varying amplitude and frequency. In each case, the proposed model is able to account for the STP effects exhibited by ZnO highlighting its neuromorphic capabilities for bio-inspired circuits. An equivalent circuit representation and the SPICE implementation of the compact model is also provided