Modeling and Characterization of Stochastic Resistive Switching in Single Ag2S Nanowires

Chalcogenide resistive switches (RS), such as Ag2S, change resistance due to the growth of metallic filaments between electrodes along the electric field gradient. Therefore, they are candidates for neuromorphic and volatile memory applications. This work analyzed the RS of individual Ag2S nanowires (NWs) and extended the basic RS model to reproduce experimental observations. The work models resistivity of the device as a percolation of the conductive filaments. It also addressed continuous fluctuations of the resistivity with a stochastic change in volume fractions of the filaments in the device. As a result, these fluctuations cause unpredictable patterns in current-voltage characteristics and include a spontaneous change in resistance of the device during the linear sweep that conventional memristor models with constant resistivity cannot represent. The parameters of the presented stochastic model of a single Ag2S NW fit the experimental data reproduced key features of RS in the physical devices. Moreover, the model suggested a non-core shell structure of the Ag2S NWs. The outcome of this work is aimed to aid in simulating large self-assembled memristive networks and help to extend existing RS models.

Mixed Conductivity of Hybrid Halide Perovskites: Emerging Opportunities and Challenges

Hybrid halide perovskites feature mixed ionic-electronic conductivities that are enhanced under device operating conditions. This has been extensively investigated over the past years by a wide range of techniques. In particular, the suppression of ionic motion by means of material and device engineering has been of increasing interest, such as through compositional engineering, using molecular modulators as passivation agents, and low-dimensional perovskite materials in conjunction with alternative device architectures to increase the stabilities under ambient and operating conditions of voltage bias and light. While this remains an ongoing challenge for photovoltaics and light-emitting diodes, mixed conductivities offer opportunities for hybrid perovskites to be used in other technologies, such as rechargeable batteries and resistive switches for neuromorphic memory elements. This article provides an overview of the recent developments with a perspective on the emerging utility in the future.

A Leaky-Integrate-and-Fire Neuron Model of Spontaneous Reset of Thin-Film Metal-Oxide Resistive Switches

Resistive switches in crossbar arrays introduce one potential option to push past the limits of CMOS process scaling, with advantages including low switching thresholds (<3 V), high integrability with CMOS, and fast switching speeds (<10 ns). These typically employ a 1T1R scheme for each cell, where the transistor is deployed for selection and sneak path mitigation. However, when conductive filaments are formed in metal-oxide resistive switches, it is often the case that analog states are not thermodynamically favorable, and will spontaneously set or reset to a more stable state. This causes stochastic switching, variability, and non-reproducibility, in a manner which cannot be harnessed in stochastic gradient descent. Equally important is the memory leakage problem that is introduced. In this work, we present a generalized neuron model of resistive switching in the development of a phase plane characterization, and verify its operation by comparing it to our own in-house fabricated thin-film titanium-oxide memristor array. We show an alternative design methodology that draws inspiration from the leaky-integrate-and-fire neuron model. The advantages exhibited by such a methodology are to provide more biologically accurate neuronal model and to enable large scale simulations, demonstrated by the 30% improvement in speed over similar device models.

Picosecond multilevel resistive switching in tantalum oxide thin films

The increasing demand for high-density data storage leads to an increasing interest in novel memory concepts with high scalability and the opportunity of storing multiple bits in one cell. A promising candidate is the redox-based resistive switch repositing the information in form of different resistance states. For reliable programming, the underlying physical parameters need to be understood. We reveal that the programmable resistance states are linked to internal series resistances and the fundamental nonlinear switching kinetics. The switching kinetics of $$\hbox {Ta}_2 \hbox {O}_5$$ Ta 2 O 5 -based cells was investigated in a wide range over 15 orders of magnitude from 10$$^5$$ 5 s to 250 ps. The capacitive charging time of our device limits the direct observation of the set time below 770 ps, however, we found indication for an intrinsic switching speed of 10 ps at a stimulus of 3 V. On all time scales, multi-bit data storage capabilities were demonstrated. The elucidated link between fundamental material properties and multi-bit data storage paves the way for designing resistive switches for memory and neuromorphic applications.