Perovskite-Based Memristor with 50-Fold Switchable Photosensitivity for In-Sensor Computing Neural Network

High-Entropy Perovskite Thin Film in the Gd-Nd-Sm-La-Y-Co System: Deposition, Structure and Optoelectronic Properties

Multicomponent equimolar perovskite oxides (ME-POs) have recently emerged as a highly promising class of materials with unique synergistic effects, making them well-suited for applications in such areas as photovoltaics and micro- and nanoelectronics. High-entropy perovskite oxide thin film in the (Gd_0.2Nd_0.2La_0.2Sm_0.2Y_0.2)CoO₃ (RECO, where RE = Gd_0.2Nd_0.2La_0.2Sm_0.2Y_0.2, C = Co, and O = O₃) system was synthesized via pulsed laser deposition. The crystalline growth in an amorphous fused quartz substrate and single-phase composition of the synthesized film was confirmed by X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). Surface conductivity and activation energy were determined using a novel technique implementing atomic force microscopy (AFM) in combination with current mapping. The optoelectronic properties of the deposited RECO thin film were characterized using UV/VIS spectroscopy. The energy gap and nature of optical transitions were calculated using the Inverse Logarithmic Derivative (ILD) and four-point resistance method, suggesting direct allowed transitions with altered dispersions. The narrow energy gap of RECO, along with its relatively high absorption properties in the visible spectrum, positions it as a promising candidate for further exploration in the domains of low-energy infrared optics and electrocatalysis.

Organic Memristor with Synaptic Plasticity for Neuromorphic Computing Applications

Memristors have been considered to be more efficient than traditional Complementary Metal Oxide Semiconductor (CMOS) devices in implementing artificial synapses, which are fundamental yet very critical components of neurons as well as neural networks. Compared with inorganic counterparts, organic memristors have many advantages, including low-cost, easy manufacture, high mechanical flexibility, and biocompatibility, making them applicable in more scenarios. Here, we present an organic memristor based on an ethyl viologen diperchlorate [EV(ClO₄)]₂/triphenylamine-containing polymer (BTPA-F) redox system. The device with bilayer structure organic materials as the resistive switching layer (RSL) exhibits memristive behaviors and excellent long-term synaptic plasticity. Additionally, the device's conductance states can be precisely modulated by consecutively applying voltage pulses between the top and bottom electrodes. A three-layer perception neural network with in situ computing enabled was then constructed utilizing the proposed memristor and trained on the basis of the device's synaptic plasticity characteristics and conductance modulation rules. Recognition accuracies of 97.3% and 90% were achieved, respectively, for the raw and 20% noisy handwritten digits images from the Modified National Institute of Standards and Technology (MNIST) dataset, demonstrating the feasibility and applicability of implementing neuromorphic computing applications utilizing the proposed organic memristor.

Metal-Organic Frameworks-Based Memristors: Materials, Devices, and Applications

Facing the explosive growth of data, a number of new micro-nano devices with simple structure, low power consumption, and size scalability have emerged in recent years, such as neuromorphic computing based on memristor. The selection of resistive switching layer materials is extremely important for fabricating of high performance memristors. As an organic-inorganic hybrid material, metal-organic frameworks (MOFs) have the advantages of both inorganic and organic materials, which makes the memristors using it as a resistive switching layer show the characteristics of fast erasing speed, outstanding cycling stability, conspicuous mechanical flexibility, good biocompatibility, etc. Herein, the recent advances of MOFs-based memristors in materials, devices, and applications are summarized, especially the potential applications of MOFs-based memristors in data storage and neuromorphic computing. There also are discussions and analyses of the challenges of the current research to provide valuable insights for the development of MOFs-based memristors.