Magnetism modulation and conductance quantization in a gadolinium oxide memristor

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

Uniform multilevel switching and synaptic properties in RF-sputtered InGaZnO-based memristor treated with oxygen plasma

Bipolar gradual resistive switching was investigated in ITO/InGaZnO/ITO resistive switching devices. Controlled intrinsic oxygen vacancy formation inside the switching layer enabled the establishment of a stable multilevel memory state, allowing for RESET voltage control and non-degradable data endurance. The ITO/InGaZnO interface governs the migration of oxygen ions and redox reactions within the switching layer. Voltage-stress-induced electron trapping and oxygen vacancy formation were observed before conductive filament electroforming. This device mimicked biological synapses, demonstrating short- and long-term potentiation and depression through electrical pulse sequences. Modulation of post-synaptic currents and pulse frequency-dependent short-term potentiation were successfully emulated in the InGaZnO-based artificial synapse. The ITO/InGaZnO/ITO memristor exhibited spike-amplitude-dependent plasticity, spike-rate-dependent plasticity, and potentiation-depression synaptic learning with low energy consumption, making it a promising candidate for large-scale integration.

A magnetic field controlled memristor towards the design of an implantable detector

Memristors, which combine the behaviors of memory and resistive switching (RS), have a wide application prospect in information processing and artificial neural networks. The RS memory behaviors of memristors are primarily determined by the functional layer materials, device structure, and working conditions. Herein, a CuMnO₂ nanomaterial with the manganese copper ore structure was prepared on a Ti substrate by hydrothermal method, and a memristor with the Ag/CuMnO₂/Ti sandwich structure was developed. The RS memory behavior of the as-prepared memristor can be regulated through a low magnetic field (MF), and thus the resistance value of device shows a multi-level resistance states. Compared with other regulation factors, the MF can remotely adjust and control the RS characteristics of memristor, which is a non-invasive and non-destructive regulatory means. The MF regulated memristor can not only be used for multi-level high-density information storage, but also it can protect the health of special populations by identifying the MF intensity of the surrounding environment. When the device is operated in an MF environment, the change of resistance value of the device in both high resistance state (HRS) and low resistance state (LRS) is mainly attributed to the influence of Loren magnetic force on conductive ions.

Quantum Conductance in Memristive Devices: Fundamentals, Developments, and Applications

Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements that exhibit resistive switching, which relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next-generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, a comprehensive review on memristive quantum devices, where quantum conductance effects can be observed by coupling ionics with electronics, is presented. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability, and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, discussing how memristive devices with controllable atomic-sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature.

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al₂O₃ interlayer between the HfO₂ switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO₂/Al₂O₃/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 10⁵ and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be - 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 10⁵ AC cycles, and a retention time of 10⁴ s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.