Nanomaterials for Flexible Neuromorphics.

The quest to imbue machines with intelligence akin to that of humans, through the development of adaptable neuromorphic devices and the creation of artificial neural systems, has long stood as a pivotal goal in both scientific inquiry and industrial advancement. Recent advancements in flexible neuromorphic electronics primarily rely on nanomaterials and polymers owing to their inherent uniformity, superior mechanical and electrical capabilities, and versatile functionalities. However, this field is still in its nascent stage, necessitating continuous efforts in materials innovation and device/system design.

An Antagonistic Photovoltaic Memristor for Bioinspired Active Contrast Adaptation.

Machine vision systems that consist of cameras and image-processing components for visual inspection and identification tasks play a critical role in various intelligent applications, including pilotless vehicles and surveillance systems. However, current systems usually possess a limited dynamic range and fixed photoresponsivity, restricting their capability of gaining high-fidelity images when encoding a high-contrast scene. Here, it is shown that a photovoltaic memristor incorporating two antagonistic photovoltaic junctions can autonomously adjust its response to varying light stimuli, enabling the amplification of shadows and inhibition of highlight saturation.

Ferroelectric Polarization Enhanced Optoelectronic Synaptic Response of a CuInPS Transistor Structure.

Neuromorphic computing can simulate brain function and is a pivotal element in next-generation computing, providing a potential solution to the limitations brought by the von Neumann bottleneck. Optoelectronic synaptic devices are highly promising tools for simulating biomimetic nervous systems. In this study, we developed an optoelectronic neuromorphic device with a transistor structure constructed using ferroelectric CuInPS.

A Near-Infrared Retinomorphic Device with High Dimensionality Reservoir Expression.

Physical reservoir-based reservoir computing (RC) systems for intelligent perception have recently gained attention because they require fewer computing resources. However, the system remains limited in infrared (IR) machine vision, including materials and physical reservoir expression power. Inspired by biological visual perception systems, the study proposes a near-infrared (NIR) retinomorphic device that simultaneously perceives and encodes narrow IR spectral information (at ≈980 nm).

Dual-Mode Reconfigurable Split-Gate Logic Transistor through Van der Waals Integration.

As silicon-based transistors approach their physical size limitations, two-dimensional material-based reconfigurable functional electronic devices are considered the most promising novel device architectures beyond Moore strategies. While these devices have garnered significant attention, they often require complex device fabrication processes and extra electric fields. Additionally, the device performance is usually limited by the metal-semiconductor interface properties.

In-Sublattice Carrier Transition Enabled Polarimetric Photodetectors with Reconfigurable Polarity Transition.

Miniaturized polarimetric photodetectors based on anisotropic two-dimensional materials attract potential applications in ultra-compact polarimeters. However, these photodetectors are hindered by the small polarization ratio values and complicated artificial structures. Here, a novel polarization photodetector based on in-sublattice carrier transition in the CdSbSeBr/WSe heterostructure, with a giant and reconfigurable PR value, is demonstrated.

Development of Bio-Voltage Operated Humidity-Sensory Neurons Comprising Self-Assembled Peptide Memristors.

Biomimetic humidity sensors offer a low-power approach for respiratory monitoring in early lung-disease diagnosis. However, balancing miniaturization and energy efficiency remains challenging. This study addresses this issue by introducing a bioinspired humidity-sensing neuron comprising a self-assembled peptide nanowire (NW) memristor with unique proton-coupled ion transport.

An in-sensor humidity computing system for contactless human-computer interaction.

Being capable of processing large amounts of redundant data and decreasing power consumption, in-sensor computing approaches play significant roles in neuromorphic computing and are attracting increasing interest in perceptual information processing. Herein, we proposed a high performance humidity-sensitive memristor based on a Ti/graphene oxide (GO)/HfO/Pt structure and verified its potential for application in remote health management and contactless human-machine interfaces. Since GO possesses abundant hydrophilic groups (carbonyl, epoxide, and hydroxyl), the memristor shows a high humidity sensitivity, fast response, and wide response range.

Ni Single-Atoms Based Memristors with Ultrafast Speed and Ultralong Data Retention.

Memristor with low-power, high density, and scalability fulfills the requirements of the applications of the new computing system beyond Moore's law. However, there are still nonideal device characteristics observed in the memristor to be solved. The important observation is that retention and speed are correlated parameters of memristor with trade off against each other.

Porous crystalline materials for memories and neuromorphic computing systems.

Porous crystalline materials usually include metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs) and zeolites, which exhibit exceptional porosity and structural/composition designability, promoting the increasing attention in memory and neuromorphic computing systems in the last decade. From both the perspective of materials and devices, it is crucial to provide a comprehensive and timely summary of the applications of porous crystalline materials in memory and neuromorphic computing systems to guide future research endeavors. Moreover, the utilization of porous crystalline materials in electronics necessitates a shift from powder synthesis to high-quality film preparation to ensure high device performance.

Covalent Organic Frameworks for Neuromorphic Devices.

Neuromorphic computing could enable the potential to break the inherent limitations of conventional von Neumann architectures, which has led to widespread research interest in developing novel neuromorphic memory devices, such as memristors and bioinspired artificial synaptic devices. Covalent organic frameworks (COFs), as crystalline porous polymers, have tailorable skeletons and pores, providing unique platforms for the interplay with photons, excitons, electrons, holes, ions, spins, and molecules. Such features encourage the rising research interest in COF materials in neuromorphic electronics.

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision.

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway.

Biocompatible Material-Based Flexible Biosensors: From Materials Design to Wearable/Implantable Devices and Integrated Sensing Systems.

Human beings have a greater need to pursue life and manage personal or family health in the context of the rapid growth of artificial intelligence, big data, the Internet of Things, and 5G/6G technologies. The application of micro biosensing devices is crucial in connecting technology and personalized medicine. Here, the progress and current status from biocompatible inorganic materials to organic materials and composites are reviewed and the material-to-device processing is described.

Electrical Contacts With 2D Materials: Current Developments and Future Prospects.

Current electrical contact models are occasionally insufficient at the nanoscale owing to the wide variations in outcomes between 2D mono and multi-layered and bulk materials that result from their distinctive electrostatics and geometries. Contrarily, devices based on 2D semiconductors present a significant challenge due to the requirement for electrical contact with resistances close to the quantum limit. The next generation of low-power devices is already hindered by the lack of high-quality and low-contact-resistance contacts on 2D materials.

A Perovskite Memristor with Large Dynamic Space for Analog-Encoded Image Recognition.

Reservoir computing (RC) is a computational architecture capable of efficiently processing temporal information, which allows low-cost hardware implementation. However, the previously reported memristor-based RC mostly utilized binarized data sets to reduce the difficulty of signal processing of the memristor, which inevitably induces data distortion to a certain extent, leading to poor network computing performance. Here, we report on a RC system in a fully memristive architecture based on solution-processed perovskite memristors.

Flexible Floating-Gate Electric-Double-Layer Organic Transistor for Neuromorphic Computing.

The key to the study of flexible neuromorphic computing is the excellent weight update characteristic of neuromorphic devices. Electric-double-layer transistors (EDLTs) include high transconductance, excellent stability of threshold voltage, linear weight updates, and repetitive ion-concentration-dependent switching properties. However, up to now, there is no report on a flexible EDLT that provides all the aforementioned performance characteristics.

Emerging 2D Metal Oxides: From Synthesis to Device Integration.

2D metal oxides have aroused increasing attention in the field of electronics and optoelectronics due to their intriguing physical properties. In this review, an overview of recent advances on synthesis of 2D metal oxides and their electronic applications is presented. First, the tunable physical properties of 2D metal oxides that relate to the structure (various oxidation-state forms, polymorphism, etc.

2D Layers of Group VA Semiconductors: Fundamental Properties and Potential Applications.

Members of the 2D group VA semiconductors (phosphorene, arsenene, antimonene, and bismuthine) present a new class of 2D materials, which are recently gaining a lot of research interest. These materials possess layered morphology, tunable direct bandgap, high charge carrier mobility, high stability, unique in-plane anisotropy, and negative Poisson's ratio. They prepare the ground for novel and multifunctional applications in electronics, optoelectronics, and batteries.

Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity.

Get in-depth understanding of each part of visual pathway yields insights to conquer the challenges that classic computer vision is facing. Here, we first report the bioinspired striate cortex with binocular and orientation selective receptive field based on the crossbar array of self-powered memristors which is solution-processed monolithic all-perovskite system with each cross-point containing one CsFAPbI solar cell directly stacking on the CsPbBrI memristor. The plasticity of self-powered memristor can be modulated by optical stimuli following triplet-STDP rules.

The floating body effect of a WSe transistor with volatile memory performance.

The floating body effect in Meta-Stable-Dip RAM (MSDRAM) has been broadly employed in implementing single-transistor capacitor-less (1T0C) dynamic random access memory (DRAM) cells to break through the limitation of finite size reduction of peripheral capacitors. However, the majority of them were broadly demonstrated in conventional CMOS technology, while emerging semiconductor systems are rarely explored. Here, we creatively explore exfoliated multilayer tungsten diselenide (WSe) for the application of 1T0C DRAM, breaking the limitation of channel thickness in the traditional architecture.

Enhanced electrical and thermal properties of semi-conductive PANI-CNCs with surface modified CNCs.

Cellulose nanocrystals (CNCs) are the most commonly used natural polymers for biomaterial synthesis. However, their low dispersibility, conductivity, and poor compatibility with the hydrophobic matrix hinder their potential applications. Therefore, we grafted sulfate half-ester and carboxylic functional groups onto CNC surfaces (S-CNC and C-CNC) to overcome these shortcomings.