Memristive Devices with Highly Repeatable Analog States Boosted by Graphene Quantum Dots

Antiferroelectric Heterostructures Memristors with Unique Resistive Switching Mechanisms and Properties

A novel antiferroelectric material, PbSnO3 (PSO), was introduced into a resistive random access memory (RRAM) to reveal its resistive switching (RS) properties. It exhibits outstanding electrical performance with a large memory window (>104), narrow switching voltage distribution (±2 V), and low power consumption. Using high-resolution transmission electron microscopy, we observed the antiferroelectric properties and remanent polarization of the PSO thin films. The in-plane shear strains in the monoclinic PSO layer are attributed to oxygen octahedral tilts, resulting in misfit dislocations and grain boundaries at the PSO/SRO interface. Furthermore, the incoherent grain boundaries between the orthorhombic and monoclinic phases are assumed to be the primary paths of Ag+ filaments. Therefore, the RS behavior is primarily dominated by antiferroelectric polarization and defect mechanisms for the PSO structures. The RS behavior of antiferroelectric heterostructures controlled by switching spontaneous polarization and strain, defects, and surface chemistry reactions can facilitate the development of new antiferroelectric device systems.

Wide-Range Synaptic Current Responses with a Liquid Ga Electrode via a Surface Redox Reaction in a NaOH Solution at Different Molar Concentrations

A liquid Ga-based synaptic device with two-terminal electrodes is demonstrated in NaOH solutions at 50 °C. The proposed electrochemical redox device using the liquid Ga electrode in the NaOH solution can emulate various biological synapses that require different decay constants. The device exhibits a wide range of current decay times from 60 to 320 ms at different NaOH mole concentrations from 0.2 to 1.6 M. This research marks a step forward in the development of flexible and biocompatible neuromorphic devices that can be utilized for a range of applications where different synaptic strengths are required lasting from a few milliseconds to seconds.

Resistive Switching Crossbar Arrays Based on Layered Materials

Resistive switching (RS) devices are metal/insulator/metal cells that can change their electrical resistance when electrical stimuli are applied between the electrodes, and they can be used to store and compute data. Planar crossbar arrays of RS devices can offer a high integration density (>10⁸  devices mm^{- 2} ) and this can be further enhanced by stacking them three-dimensionally. The advantage of using layered materials (LMs) in RS devices compared to traditional phase-change materials and metal oxides is that their electrical properties can be adjusted with a higher precision. Here, the key figures-of-merit and procedures to implement LM-based RS devices are defined. LM-based RS devices fabricated using methods compatible with industry are identified and discussed. The focus is on small devices (size < 9 µm² ) arranged in crossbar structures, since larger devices may be affected by artifacts, such as grain boundaries and flake junctions. How to enhance device performance, so to accelerate the development of this technology, is also discussed.

Nanomaterials and their applications on bio-inspired wearable electronics

Wearable electronics featuring conformal attachment, sensitive perception and intellectual signal processing have made significant progress in recent years. However, when compared with living organisms, artificial sensory devices showed undeniable bulky shape, poor adaptability, and large energy consumption. To make up for the deficiencies, biological examples provide inspirations of novel designs and practical applications. In the field of biomimetics, nanomaterials from nanoparticles to layered two-dimensional materials are actively involved due to their outstanding physicochemical properties and nanoscale configurability. This review focuses on nanomaterials related to wearable electronics through bioinspired approaches on three different levels, interfacial packaging, sensory structure, and signal processing, which comprehensively guided recent progress of wearable devices in leveraging both nanomaterial superiorities and biorealistic functionalities. In addition, opinions on potential development trend are proposed aiming at implementing bioinspired electronics in multifunctional portable sensors, health monitoring, and intelligent prosthetics.

Hybrid oxide brain-inspired neuromorphic devices for hardware implementation of artificial intelligence

The state-of-the-art artificial intelligence technologies mainly rely on deep learning algorithms based on conventional computers with classical von Neumann computing architectures, where the memory and processing units are separated resulting in an enormous amount of energy and time consumed in the data transfer process. Inspired by the human brain acting like an ultra-highly efficient biological computer, neuromorphic computing is proposed as a technology for hardware implementation of artificial intelligence. Artificial synapses are the main component of a neuromorphic computing architecture. Memristors are considered to be a relatively ideal candidate for artificial synapse applications due to their high scalability and low power consumption. Oxides are most widely used in memristors due to the ease of fabrication and high compatibility with complementary metal-oxide-semiconductor processes. However, oxide memristors suffer from unsatisfactory stability and reliability. Oxide-based hybrid structures can effectively improve the device stability and reliability, therefore providing a promising prospect for the application of oxide memristors to neuromorphic computing. This work reviews the recent advances in the development of hybrid oxide memristive synapses. The discussion is organized according to the blending schemes as well as the working mechanisms of hybrid oxide memristors.

Fabrication and investigation of quaternary Ag-In-Zn-S quantum dots-based memristors with ultralow power and multiple resistive switching behaviors

Quaternary Ag-In-Zn-S (AIZS) quantum dots (QDs) play critical roles in various applications since they have advantages of combining superior optical and electrical features, such as tunable fluorescence emission and high carrier mobilities. However, the application of semiconductor AIZS QDs in brain-inspired devices (e.g. memristor) has been rarely reported. In this work, the tunable volatile threshold switching (TS) and non-volatile memory switching (MS) behaviors have been obtained in a memristor composed of AIZS QDs by regulating the magnitude of compliance current. Additionally, the innovative Ag/AIZS structure devices without traditional oxide layer exhibit low operation voltage (∼0.25 V) and programming current (100 nA) under the TS mode. Moreover, the devices achieve reproducible bipolar resistive switching (RS) behaviors with large ON/OFF ratio of ∼10⁵, ultralow power consumption of ∼10^-10 W, and good device-to-device uniformity under the MS mode. Furthermore, the charge transport mechanisms of the high- and low-resistance states under the positive and negative bias have been analyzed with space-charge-limited-current and filament conduction models, respectively. This work not only validates the potential of AIZS QDs acting as dielectric layer in RS devices but also provides a new guideline for designing ultralow power and multiple RS characteristics devices.

A carbon-based memristor design for associative learning activities and neuromorphic computing

Carbon quantum dots (QDs) have attracted significant interest due to their excellent electronic properties and wide application prospects. However, the application of carbon QDs has been rarely reported in memristors. Here, a memristor model with carbon conductive filaments (CFs) is proposed for the first time based on carbon quantum dots. The CF-based devices exhibited excellent resistive switching performance, in particular a narrow range of SET and RESET voltages and good power efficiency and retention properties. These devices could also emulate important biological synapse performances, such as the transition from short-term plasticity (STP) to long-term potentiation (LTP) behaviors, long-term depression (LTD) behavior, and four types of spike-timing-dependent plasticity (STDP) learning rules. Interestingly, Pavlovian associative learning functions were also reliably demonstrated in the memristor device (MD). The digit recognition ability of the MDs was evaluated though a single-layer perceptron model, in which the recognition accuracy of digits reached 92.63% after 250 training iterations. The transmission electron microscopy (TEM) results evidenced that the carbon CF was found in the MD at the "ON" state. Thus, this new carbon CF-based mechanism for memristors provides a new idea for achieving better neuromorphic MDs and applications.

Robust Ag/ZrO2/WS2/Pt Memristor for Neuromorphic Computing

The development of the information age has made resistive random access memory (RRAM) a critical nanoscale memristor device (MD). However, due to the randomness of the area formed by the conductive filaments (CFs), the RRAM MD still suffers from a problem of insufficient reliability. In this study, the memristor of Ag/ZrO₂/WS₂/Pt structure is proposed for the first time, and a layer of two-dimensional (2D) WS₂ nanosheets was inserted into the MD to form 2D material and oxide double-layer MD (2DOMD) to improve the reliability of single-layer devices. The results indicate that the electrochemical metallization memory cell exhibits a highly stable memristive switching and concentrated ON- and OFF-state voltage distribution, high speed (∼10 ns), and robust endurance (>10⁹ cycles). This result is superior to MDs with a single-layer ZrO₂ or WS₂ film because two layers have different ion transport rates, thereby limiting the rupture/rejuvenation of CFs to the bilayer interface region, which can greatly reduce the randomness of CFs in MDs. Moreover, we used the handwritten recognition dataset (i.e., the Modified National Institute of Standards and Technology (MNIST) database) for neuromorphic simulations. Furthermore, biosynaptic functions and plasticity, including spike-timing-dependent plasticity and paired-pulse facilitation, have been successfully achieved. By incorporating 2D materials and oxides into a double-layer MD, the practical application of RRAM MD can be significantly enhanced to facilitate the development of artificial synapses for brain-enhanced computing systems in the future.

Self-Assembled Networked PbS Distribution Quantum Dots for Resistive Switching and Artificial Synapse Performance Boost of Memristors

With the advent of the era of big data, resistive random access memory (RRAM) has become one of the most promising nanoscale memristor devices (MDs) for storing huge amounts of information. However, the switching voltage of the RRAM MDs shows a very broad distribution due to the random formation of the conductive filaments. Here, self-assembled lead sulfide (PbS) quantum dots (QDs) are used to improve the uniformity of switching parameters of RRAM, which is very simple comparing with other methods. The resistive switching (RS) properties of the MD with the self-assembled PbS QDs exhibit better performance than those of MDs with pure-Ga₂ O₃ and randomly distributed PbS QDs, such as a reduced threshold voltage, uniformly distributed SET and RESET voltages, robust retention, fast response time, and low power consumption. This enhanced performance may be attributed to the ordered arrangement of the PbS QDs in the self-assembled PbS QDs which can efficiently guide the growth direction for the conducting filaments. Moreover, biosynaptic functions and plasticity, are implemented successfully in the MD with the self-assembled PbS QDs. This work offers a new method of improving memristor performance, which can significantly expand existing applications and facilitate the development of artificial neural systems.

Large magnetization modulation in ZnO-based memory devices with embedded graphene quantum dots

Magnetization modulation in oxide-based resistive random-access memories facilitates their application in multifunctional memory devices and spintronics. However, the small magnetization modulation in oxide films hinders their practical applications. In this paper, we report a significant enhancement in the magnetization modulation of ZnO films upon embedding graphene quantum dots (GQDs). The magnetization-modulation ratio is greater than 500% in the ZnO-GQD hybrid films under applied biases of only 0.23/-0.20 V. This magnetization-modulation ratio is the highest value reported to date in pure or magnetic-ion-doped metal-oxide films. Further analyses indicate that the exchange of oxygen between the GQDs and ZnO, under a reversible electric field, plays an important role in enhancing the magnetization modulation. This work provides a new direction for the application of GQDs.

Determination of graphene's edge energy using hexagonal graphene quantum dots and PM7 method

Graphene quantum dots (GQDs) are important for a variety of applications and designs, and the shapes of GQDs rely on the energy of their boundaries.