A Multimodal Humidity Adaptive Optical Neuron Based on a MoWS/VO Heterojunction for Vision and Respiratory Functions.

Advancements in computing have progressed from near-sensor to in-sensor computing, culminating in the development of multimodal in-memory computing, which enables faster, energy-efficient data processing by performing computations directly within the memory devices. A bio-inspired multimodal in-memory computing system capable of performing real-time low power processing of multisensory signals, lowering data conversion and transmission across several modules in conventional chips is introduced. A novel Cu/MoWS/VO/Pt based multimodal memristor is characterized by an ON/OFF ratio as high as 10 with consistent and ultralow operating voltages of ±0.

Ultralow Powered 2D MoS-Based Memristive Crossbar Array for Synaptic Applications.

Two-dimensional materials are increasingly integral to beyond-CMOS electronics, facilitating the development of emerging memristive device technology for information storage and neuromorphic computing. Despite their emergence, some critical challenges including low device yield, substantial device-to-device (D2D), and cycle-to-cycle (C2C) variability factors hinder the development of high-density memristive devices for future low-power electronic applications. Here, we demonstrate a memristive crossbar array (MCA) in which multilayer 2D MoS acts as a resistive switching layer that offers lower switching voltages with a few microseconds pulse width.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision.

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection.

Multilevel Conductance States of Vapor-Transport-Deposited SbS Memristors Achieved via Electrical and Optical Modulation.

The pursuit of advanced brain-inspired electronic devices and memory technologies has led to explore novel materials by processing multimodal and multilevel tailored conductive properties as the next generation of semiconductor platforms, due to von Neumann architecture limits. Among such materials, antimony sulfide (SbS) thin films exhibit outstanding optical and electronic properties, and therefore, they are ideal for applications such as thin-film solar cells and nonvolatile memory systems. This study investigates the conduction modulation and memory functionalities of SbS thin films deposited via the vapor transport deposition technique.

CogniFiber: Harnessing Biocompatible and Biodegradable 1D Collagen Nanofibers for Sustainable Nonvolatile Memory and Synaptic Learning Applications.

Here, resistive switching (RS) devices are fabricated using naturally abundant, nontoxic, biocompatible, and biodegradable biomaterials. For this purpose, 1D chitosan nanofibers (NFs), collagen NFs, and chitosan-collagen NFs are synthesized by using an electrospinning technique. Among different NFs, the collagen-NFs-based device shows promising RS characteristics.