Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor

A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (α‐IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike‐rate‐dependent and spike‐timing‐dependent plasticity, long‐term/short‐term plasticity (LSP and STP) and “learning‐experience” behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a “learning‐experience” function is obtained for the first time, which is thought to be related to the metastable local structures in α‐IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike‐rate‐dependent and spike‐timing‐dependent plasticity is the same on this point; and, the STP‐to‐LTP transition can occur through repeated “stimulation” training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short‐term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.     

A High‐On/Off‐Ratio Floating‐Gate Memristor Array on a Flexible Substrate via CVD‐Grown Large‐Area 2D Layer Stacking

Memristors such as phase‐change memory and resistive memory have been proposed to emulate the synaptic activities in neuromorphic systems. However, the low reliability of these types of memories is their biggest challenge for commercialization. Here, a highly reliable memristor array using floating‐gate memory operated by two terminals (source and drain) using van der Waals layered materials is demonstrated. Centimeter‐scale samples (1.5 cm × 1.5 cm) of MoS₂ as a channel and graphene as a trap layer grown by chemical vapor deposition (CVD) are used for array fabrication with Al₂O₃ as the tunneling barrier. With regard to the memory characteristics, 93% of the devices exhibit an on/off ratio of over 10³ with an average ratio of 10⁴. The high on/off ratio and reliable endurance in the devices allow stable 6‐level memory applications. The devices also exhibit excellent memory durability over 8000 cycles with a negligible shift in the threshold voltage and on‐current, which is a significant improvement over other types of memristors. In addition, the devices can be strained up to 1% by fabricating on a flexible substrate. This demonstration opens a practical route for next‐generation electronics with CVD‐grown van der Waals layered materials.     

A Digital−Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr₃-based memristor demonstrates a high ON/OFF ratio (>10³), long retention (>10⁴ s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5 × 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa⁻¹, durability of 1.5 × 10⁴ cycles, and fast response time of 2.43 ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr₃-based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5 × 5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital−analog bimodal memristor would demonstrate potential application in human–machine interaction, prosthetics, and artificial intelligence.     

An Artificial Flexible Visual Memory System Based on an UV‐Motivated Memristor

For the mimicry of human visual memory, a prominent challenge is how to detect and store the image information by electronic devices, which demands a multifunctional integration to sense light like eyes and to memorize image information like the brain by transforming optical signals to electrical signals that can be recognized by electronic devices. Although current image sensors can perceive simple images in real time, the image information fades away when the external image stimuli are removed. The deficiency between the state‐of‐the‐art image sensors and visual memory system inspires the logical integration of image sensors and memory devices to realize the sensing and memory process toward light information for the bionic design of human visual memory. Hence, a facile architecture is designed to construct artificial flexible visual memory system by employing an UV‐motivated memristor. The visual memory arrays can realize the detection and memory process of UV light distribution with a patterned image for a long‐term retention and the stored image information can be reset by a negative voltage sweep and reprogrammed to the same or an other image distribution, which proves the effective reusability. These results provide new opportunities for the mimicry of human visual memory and enable the flexible visual memory device to be applied in future wearable electronics, electronic eyes, multifunctional robotics, and auxiliary equipment for visual handicapped.     

Silk Flexible Electronics: From Bombyx mori Silk Ag Nanoclusters Hybrid Materials to Mesoscopic Memristors and Synaptic Emulators

Functionalization of flexible materials based on mesoscopic reconstruction is a key strategy in fabricating biocompatible flexible electronics. This work is to acquire new mesoscopic bioelectronic hybrid materials of silk fibroin (SF)‐Ag nanoclusters (AgNCs@BSA; BSA: bovine serum albumin), which enhance significantly the performance of silk memristors. It is to build AgNCs@BSA into SF mesoscopic networks by templated β‐crystallization. Atomic force microscopy potential probing indicates that AgNCs@BSA serve as electronic potential wells that change completely the transport behavior of charge particles within the SF films. This leads to significant enhancement in the switching speed (≈10 ns), very good switching stability, extremely low set/reset voltages (0.3/?0.18 V) of SF meso‐hybrid memristors, compared with the original and other organic memristors, and displays unique synapse characteristics and the capability of synapse learning. Classical density functional theory Poisson–Nernst–Planck simulations indicate that the enhanced performance is subject to the low potential paths interconnecting the AgNCs@BSA, which guide charges' transport (Ag⁺) and deposition in SF films.     

Silver‐Adapted Diffusive Memristor Based on Organic Nitrogen‐Doped Graphene Oxide Quantum Dots (N‐GOQDs) for Artificial Biosynapse Applications

Carbon‐based electronic devices are suitable candidates for bioinspired electronics due to their low cost, eco‐friendliness, mechanical flexibility, and compatibility with complementary metal‐oxide‐semiconductor technology. New types of materials such as graphene quantum dots (GQDs) have attracted attention in the search for new applications beyond solar cells and energy harvesting due to their superior properties such as elevated photoluminescence, high chemical inertness, and excellent biocompatibility. In this paper, a biocompatible/organic electronic synapse based on nitrogen‐doped graphene oxide quantum dots (N‐GOQDs) is reported, which exhibits threshold resistive switching via silver cation (Ag⁺) migration dynamics. In analogy to the calcium (Ca²⁺) ion dynamics of biological synapses, important biological synapse functions such as short‐term potentiation (STP), paired‐pulse facilitation, and transition from STP to long‐term plasticity behaviors are replicated. Long‐term depression behavior is also evaluated and specific spike‐timing dependent plasticity is assessed. In addition, elaborated switching mechanism of biosimilar Ag⁺ migration dynamics provides the potential for using N‐GOQD‐based artificial synapse in future biocompatible neuromorphic systems.     

Understanding Microscopic Operating Mechanisms of a van der Waals Planar Ferroelectric Memristor

Ferroelectric memristors represent a promising new generation of devices that have a wide range of applications in memory, digital information processing, and neuromorphic computing. Recently, van der Waals ferroelectric In₂Se₃ with unique interlinked out-of-plane and in-plane polarizations has enabled multidirectional resistance switching, providing unprecedented flexibility in planar and vertical device integrations. However, the operating mechanisms of these devices have remained unclear. Here, through the demonstration of van der Waals In₂Se₃-based planar ferroelectric memristors with the device resistance continuously tunable over three orders of magnitude, and by correlating device resistance states, ferroelectric domain configurations, and surface electric potential, the studies reveal that the resistive switching is controlled by the multidomain formations and the associated energy barriers between domains, as opposed to the commonly assumed Schottky barrier modulations at the metal-ferroelectric interface. The findings reveal new device physics through elucidating the microscopic operating mechanisms of this new generation of devices, and provide a critical guide for future device development and integration efforts.     

Threshold Switching of Ag or Cu in Dielectrics: Materials,Mechanism, and Applications

Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.     

Random Telegraph Noise in Metal-Oxide Memristors for True Random Number Generators: A Materials Study

Some memristors with metal/insulator/metal (MIM) structure have exhibited random telegraph noise (RTN) current signals, which makes them ideal to build true random number generators (TRNG) for advanced data encryption. However, there is still no clear guide on how essential manufacturing parameters like materials selection, thicknesses, deposition methods, and device lateral size can influence the quality of the RTN signal. In this paper, an exhaustive statistical analysis on the quality of the RTN signals produced by different MIM-like memristors is reported, and straightforward guidelines for the fabrication of memristors with enhanced RTN performance are presented, which are: i) Ni and Ti electrodes show better RTN than Au electrodes, ii) the 50 μm × 50 μm devices show better RTN than the 5 μm × 5 μm ones, iii) TiO₂ shows better RTN than HfO₂ and Al₂O₃, iv) sputtered-oxides show better RTN than ALD-oxides, and v) 10 nm thick oxides show better RTN than 5 nm thick oxides. The RTN signals recorded have been used as entropy sources in high-throughput TRNG circuits, which have passed the randomness tests of the National Institute of Standards and Technology. The work can serve as a useful guide for materials scientists and electronic engineers when fabricating MIM-like memristors for RTN applications.