An investigation of the kinetic behavior of MAPbI3 memristors shows that the onset voltage to a high conducting state depends strongly on the voltage sweep rate, and the impedance spectra generate complex capacitive and inductive patterns. We develop a dynamic model to describe these features and obtain physical insight into the coupling of ionic and electronic properties that produce the resistive switching behavior. The model separates the memristive response into distinct diffusion and transition-state-formation steps that describe well the experimental current–voltage curves at different scan rates and impedance spectra. The ac impedance analysis shows that the halide perovskite memristor response contains the composition of two inductive processes that provide a huge negative capacitance associated with inverted hysteresis. The results provide a new approach to understand some typical characteristics of halide perovskite devices, such as the inductive behavior and hysteresis effects, according to the time scales of internal processes. 

Physical Model for the Current–Voltage Hysteresis and Impedance of Halide Perovskite Memristors

A multitude of chemical, biological, and material systems present an inductive behavior that is not electromagnetic in origin. Here, it is termed a chemical inductor. We show that the structure of the chemical inductor consists of a two-dimensional system that couples a fast conduction mode and a slowing down element. Therefore, it is generally defined in dynamical terms rather than by a specific physicochemical mechanism. The chemical inductor produces many familiar features in electrochemical reactions, including catalytic, electrodeposition, and corrosion reactions in batteries and fuel cells, and in solid-state semiconductor devices such as solar cells, organic light-emitting diodes, and memristors. It generates the widespread phenomenon of negative capacitance, it causes negative spikes in voltage transient measurements, and it creates inverted hysteresis effects in current–voltage curves and cyclic voltammetry. Furthermore, it determines stability, bifurcations, and chaotic properties associated to self-sustained oscillations in biological neurons and electrochemical systems. As these properties emerge in different types of measurement techniques such as impedance spectroscopy and time-transient decays, the chemical inductor becomes a useful framework for the interpretation of the electrical, optoelectronic, and electrochemical responses in a wide variety of systems. In the paper, we describe the general dynamical structure of the chemical inductor and we comment on a broad range of examples from different research areas.

https://pubs.acs.org/doi/pdf/10.1021/jacs.2c00777

Chemical Inductor

Memristors are candidate devices for constructing artificial neurons, synapses, and computational networks for brainlike information processing and sensory-motor autonomous systems. However, the dynamics of natural neurons and synapses are challenging and cannot be well reproduced with standard electronic components. Halide perovskite memristors operate by mixed ionic–electronic properties that may lead to replicate the live computation elements. Here we explore the dynamical behavior of a halide perovskite memristor model to evaluate the response to a step perturbation and the self-sustained oscillations that produce analog neuron spiking. As the system contains a capacitor and a voltage-dependent chemical inductor, it can mimic an action potential in response to a square current pulse. Furthermore, we discover a property that cannot occur in the standard two-dimensional model systems: a three-dimensional model shows a dynamical instability that produces a spiking regime without the need for an intrinsic negative resistance. These results open a new pathway to create spiking neurons without the support of electronic circuits.

Dynamic Instability and Time Domain Response of a Model Halide Perovskite Memristor for Artificial Neurons

Spontaneous oscillations in a variety of systems, including neurons, electrochemical, and semiconductor devices, occur as a consequence of Hopf bifurcation in which the system makes a sudden transition to an unstable dynamical state by the smooth change of a parameter. We review the linear stability analysis of oscillatory systems that operate by current–voltage control using the method of impedance spectroscopy. Based on a general minimal model that contains a fast-destabilizing variable and a slow stabilizing variable, a set of characteristic frequencies that determine the shape of the spectra and the associated dynamical regimes are derived. We apply this method to several self-sustained rhythmic oscillations in the FitzHugh–Nagumo neuron, the Koper–Sluyters electrocatalytic system, and potentiostatic oscillations of a semiconductor device. There is a deep and physically grounded analogy between different oscillating systems: neurons, electrochemical, and semiconductor devices, as they are controlled by similar fundamental processes unified in the equivalent circuit representation. The unique impedance spectroscopic criteria for widely different variables and materials across several fields provide insight into the dynamical properties and enable the investigation of new systems such as artificial neurons for neuromorphic computation.

/pdf/hopf-bifurcations-in-electrochemical-neuronal-and-1o2yj6ay.pdf

Hopf bifurcations in electrochemical, neuronal, and semiconductor systems analysis by impedance spectroscopy

Metal-halide perovskites have drawn profuse attention during the past decade, owing to their excellent electrical and optical properties, facile synthesis, efficient energy conversion, and so on. Meanwhile, the development of information storage technologies and digital communications has fueled the demand for novel semiconductor materials. Low-dimensional perovskites have offered a new force to propel the developments of the memory field due to the excellent physical and electrical properties associated with the reduced dimensionality. In this review, the mechanisms, properties, as well as stability and performance of low-dimensional perovskite memories, involving both molecular-level perovskites and structure-level nanostructures, are comprehensively reviewed. The property-performance correlation is discussed in-depth, aiming to present effective strategies for designing memory devices based on this new class of high-performance materials. Finally, the existing challenges and future opportunities are presented.

Low-Dimensional Metal-Halide Perovskites as High-Performance Materials for Memory Applications.

Resistive switching random access memory (RRAM), also known as memristor, is regarded as an emerging nonvolatile memory and computing-in-memory technology to address the intrinsic physical limitations of conventional memory and the bottleneck of von Neumann architecture. In particular, halide perovskite RRAMs have attracted widespread attention in recent years because of their ionic migration nature and excellent photoelectric properties. This Perspective first provides a condensed overview of halide perovskite RRAMs based on materials, device performance, switching mechanism, and potential applications. Moreover, this Perspective attempts to detail the challenges, such as the quality of halide perovskite films, the compatible processing of device fabrication, the reliability of memory performance, and clarification of the switching mechanism, and further discusses how the outstanding challenges of halide perovskite RRAMs could be met in future research.

Kaijin Kang

Papers

Halide Perovskites for Resistive Switching Memory.