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.

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).

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.

Energy-efficient transistors: suppressing the subthreshold swing below the physical limit.

With the miniaturization of silicon-based electronic components, power consumption is becoming a fundamental issue for micro-nano electronic circuits. The main reason for this is that the scaling of the supply voltage in the ultra-large-scale integrated circuit cannot keep up with the shrinking of the characteristic size of conventional transistors due to the physical limit termed "Boltzmann Tyranny", in which a gate voltage of at least 60 mV is required to modulate the drain current by one order of magnitude. Accordingly, to solve this problem, several new transistor architectures have been designed to reduce the subthreshold swing (SS) to lower than the fundamental limitation, thus lowering the supply voltage and reducing the power consumption.

Reconfigurable 2D WSe -Based Memtransistor for Mimicking Homosynaptic and Heterosynaptic Plasticity.

The mimicking of both homosynaptic and heterosynaptic plasticity using a high-performance synaptic device is important for developing human-brain-like neuromorphic computing systems to overcome the ever-increasing challenges caused by the conventional von Neumann architecture. However, the commonly used synaptic devices (e.g.

Synaptic transistors and neuromorphic systems based on carbon nano-materials.

Carbon-based materials possessing a nanometer size and unique electrical properties perfectly address the two critical issues of transistors, the low power consumption and scalability, and are considered as a promising material in next-generation synaptic devices. In this review, carbon-based synaptic transistors were systematically summarized. In the carbon nanotube section, the synthesis of carbon nanotubes, purification of carbon nanotubes, the effect of architecture on the device performance and related carbon nanotube-based devices for neuromorphic computing were discussed.

Configurable multi-state non-volatile memory behaviors in TiC nanosheets.

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component.

Near-Infrared Annihilation of Conductive Filaments in Quasiplane MoSe /Bi Se Nanosheets for Mimicking Heterosynaptic Plasticity.

It is desirable to imitate synaptic functionality to break through the memory wall in traditional von Neumann architecture. Modulating heterosynaptic plasticity between pre- and postneurons by another modulatory interneuron ensures the computing system to display more complicated functions. Optoelectronic devices facilitate the inspiration for high-performance artificial heterosynaptic systems.

Infrared-Sensitive Memory Based on Direct-Grown MoS -Upconversion-Nanoparticle Heterostructure.

Photonic memories as an emerging optoelectronic technology have attracted tremendous attention in the past few years due to their great potential to overcome the von Neumann bottleneck and to improve the performance of serial computers. Nowadays, the decryption technology for visible light is mature in photonic memories. Nevertheless, near-infrared (NIR) photonic memristors are less progressed.

Preparation and Thermoelectric Properties of Co-Doped ZnO Synthesized by Sol-Gel.

Zinc oxide (ZnO) has attracted increasing attention as one of the most promising n-type thermo-electric materials, but its practice use was limited by high thermal conductivity and low electrical conductivity. Therefore, we herein prepared Co-doped ZnO nanoparticles by sol-gel method and then compressed nanoparticles into bulk materials through spark plasma sintering. The thermo-electric properties, including electrical conductivity, Seebeck coefficient, thermal conductivity, and ZT value, have been investigated.

In situ precipitation of Te nanoparticles in p-type BiSbTe and the effect on thermoelectric performance.

Through zone melting method, a certain amount of Te nano precipitations were in situ generated in the p-type BiSbTe matrix because of the addition of graphene. Both the microstructure and thermoelectric performance were investigated. Increased carrier concentration was obtained to improve the electrical performance, and the lattice thermal conductivity was simultaneously lowered about 25% by Te nano precipitations as phonon scattering centers.