In-Depth Physical Mechanism Analysis and Wearable Applications of HfOx-Based Flexible Memristors

Multilevel resistive switching in hydrothermally synthesized FeWO4 thin film-based memristive device for non-volatile memory application

The memristors offer significant advantages as a key element in non-volatile and brain-inspired neuromorphic systems because of their salient features such as remarkable endurance, ability to store multiple bits, fast operation speed, and extremely low energy usage. This work reports the resistive switching (RS) characteristics of the hydrothermally synthesized iron tungstate (FeWO4) based thin film memristive device. The detailed physicochemical analysis was investigated using Rietveld's refinement, X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FE-SEM), and transmission electron microscopy (TEM) techniques. The fabricated Ag/FWO/FTO memristive device exhibits bipolar resistive switching (BRS) behavior. In addition, the devices exhibit negative differential resistance (NDR) at both positive and negative bias. The charge-flux relation portrayed the non-ideal or memristive nature of the devices. The reliability in the RS process was analyzed in detail using Weibull distribution and time series analysis techniques. The device exhibits stable and multilevel endurance and retention characteristics which demonstrates the suitability of the device for the high-density non-volatile memory application. The current conduction of the device was dominated by Ohmic and trap controlled-space charge limited current (TC-SCLC) mechanisms and filamentary RS process responsible for the BRS in the device. In a nutshell, the present investigations reveal the potential use of the iron tungstate for the fabrication of memristive devices for the non-volatile memory application.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

Reconfigurable Ag/HfO2/NiO/Pt Memristors with Stable Synchronous Synaptic and Neuronal Functions for Renewable Homogeneous Neuromorphic Computing System

Artificial synapses and bionic neurons offer great potential in highly efficient computing paradigms. However, complex requirements for specific electronic devices in neuromorphic computing have made memristors face the challenge of process simplification and universality. Herein, reconfigurable Ag/HfO2/NiO/Pt memristors are designed for feasible switching between volatile and nonvolatile modes by compliance current controlled Ag filaments, which enables stable and reconfigurable synaptic and neuronal functions. A neuromorphic computing system effectively replicates the biological synaptic weight alteration and continuously accomplishes excitation and reset of artificial neurons, which consist of bionic synapses and artificial neurons based on isotype Ag/HfO2/NiO/Pt memristors. This reconfigurable electrical performance of the Ag/HfO2/NiO/Pt memristors takes advantage of simplified hardware design and delivers integrated circuits with high density, which exhibits great potency for future neural networks.

Transmission Mechanism and Logical Operation of Graphene-Doped Poly(vinyl alcohol) Composite-Based Memristor

Memristors are considered the best candidates for nonvolatile memory and advanced computing technologies, and polymer and two-dimensional (2D) materials have been developed as functional layer materials in memristors with high-performance resistive switching characteristics. In this work, a polymer memristor with a graphene (Gr)-doped poly(vinyl alcohol) (PVA) composite acting as the functional layer was prepared. The memristor device exhibited superior performance with good retention and a comparatively large ON/OFF ratio at room temperature. Additionally, excellent logic operations were achieved. These satisfactory properties can be attributed to trap-induced carrier trapping and detrapping. In addition, the device exhibited stable bipolar resistive switching behavior over a moderate temperature range. This work provides insight into the transmission mechanism of polymer-based memristors and the reasons why they become unstable at high temperatures, demonstrating the potential applications of PVA-Gr-based polymer memristors as logic circuit units in integrated chips and artificial intelligence.

Internal Resistor Effect of Multilayer-Structured Synaptic Device for Low-Power Operation

A synaptic device with a multilayer structure is proposed to reduce the operating power of neuromorphic computing systems while maintaining a high-density integration. A simple metal-insulator-metal (MIM)-structured multilayer synaptic device is developed using an 8-inch wafer-based and complementary metal-oxide-semiconductor (CMOS) fabrication process. The three types of MIM-structured synaptic devices are compared to assess their effects on reducing the operating power. The obtained results exhibited low-power operation owing to the inserted layers acting as an internal resistor. The modulated operational conductance level and simple MIM structure demonstrate the feasibility of implementing both low-power operation and high-density integration in multilayer synaptic devices.

Resistive switching modulation by incorporating thermally enhanced layer in HfO2-based memristor

Oxide-based memristors by incorporating thermally enhanced layer (TEL) have showed great potential in electronic devices for high-efficient and high-density neuromorphic computing owing to the improvement of multilevel resistive switching. However, research on the mechanism of resistive switching regulation is still lacking. In this work, based on the method of finite element numerical simulation analysis, a bilayer oxide-based memristor Pt/HfO2(5 nm)/Ta2O5(5 nm)/Pt with the Ta2O5TEL was proposed. The oxygen vacancy concentrates distribution shows that the fracture of conductive filaments (CF) is at the interface where the local temperature is the highest during the reset process. The multilevel resistive switching properties were also obtained by applying different stop voltages. The fracture gap of CF can be enlarged with the increase of the stopping voltage, which is attributed to the heat-gathering ability of the TEL. Moreover, it was found that the fracture position of oxygen CF is dependent on the thickness of TEL, which exhibits a modulation of device RS performance. These results provide a theoretical guidance on the suitability of memristor devices for use in high-density memory and brain-actuated computer systems.

Improved resistive switching performance and realized electric control of exchange bias in a NiO/HfO2 bilayer structure

The fluctuation of switching parameters is unavoidable in conductive filaments (CFs)-type resistive switching (RS) devices, which restricts the application in resistive random-access memory. Here, we employed an uninsulated antiferromagnetic (AFM) NiO layer adhered to a well-insulating HfO2 layer to effectively suppress the RS fluctuation by achieving forming-free, narrower set voltage distribution, a more stable on/off ratio, and better endurance in comparison with single-HfO2-layer based RS devices. The conduction scaling behavior indicates that the NiO/HfO2 bilayer has a smaller scale parameter S0 (lateral dimension of the bottleneck for the CFs). Besides this, considering some preexisting conductive paths in the NiO layer, the electric fields and the formation/rupture of CFs can be highly localized, leading to reduced switching fluctuation and improved RS performance in the NiO/HfO2-based RS devices. Moreover, asymmetric I-V curves measured in a high resistance state (HRS) in positively and negatively biased regions and the electric modulation of exchange bias (EB) arising from the Co-NiO interfacial coupling are favorable for revealing the inherent mechanism for RS. The coexistence of RS and EB is also useful to the design of novel multifunctional memory devices.

Memristor-based neural networks: a bridge from device to artificial intelligence

Since the beginning of the 21st century, there is no doubt that the importance of artificial intelligence has been highlighted in many fields, among which the memristor-based artificial neural network technology is expected to break through the limitation of von Neumann so as to realize the replication of the human brain by enabling strong parallel computing ability and efficient data processing and become an important way towards the next generation of artificial intelligence. A new type of nanodevice, namely memristor, which is based on the variability of its resistance value, not only has very important applications in nonvolatile information storage, but also presents obsessive progressiveness in highly integrated circuits, making it one of the most promising circuit components in the post-Moore era. In particular, memristors can effectively simulate neural synapses and build neural networks; thus, they can be applied for the preparation of various artificial intelligence systems. This study reviews the research progress of memristors in artificial neural networks in detail and highlights the structural advantages and frontier applications of neural networks based on memristors. Finally, some urgent problems and challenges in current research are summarized and corresponding solutions and future development trends are put forward.