Multiwavelength Optoelectronic Synapse with 2D Materials for Mixed-Color Pattern Recognition.

Neuromorphic visual systems emulating biological retina functionalities have enormous potential for in-sensor computing, with prospects of making artificial intelligence ubiquitous. Conventionally, visual information is captured by an image sensor, stored by memory units, and eventually processed by the machine learning algorithm. Here, we present an optoelectronic synapse device with multifunctional integration of all the processes required for real time object identification.

MoS Synapses with Ultra-low Variability and Their Implementation in Boolean Logic.

Brain-inspired computing enabled by memristors has gained prominence over the years due to the nanoscale footprint and reduced complexity for implementing synapses and neurons. The demonstration of complex neuromorphic circuits using conventional materials systems has been limited by high cycle-to-cycle and device-to-device variability. Two-dimensional (2D) materials have been used to realize transparent, flexible, ultra-thin memristive synapses for neuromorphic computing, but with limited knowledge on the statistical variation of devices.

Optoelectronic synapse using monolayer MoS field effect transistors.

Optical data sensing, processing and visual memory are fundamental requirements for artificial intelligence and robotics with autonomous navigation. Traditionally, imaging has been kept separate from the pattern recognition circuitry. Optoelectronic synapses hold the special potential of integrating these two fields into a single layer, where a single device can record optical data, convert it into a conductance state and store it for learning and pattern recognition, similar to the optic nerve in human eye.

Two-Dimensional Near-Atom-Thickness Materials for Emerging Neuromorphic Devices and Applications.

Two-dimensional (2D) layered materials and their heterostructures have recently been recognized as promising building blocks for futuristic brain-like neuromorphic computing devices. They exhibit unique properties such as near-atomic thickness, dangling-bond-free surfaces, high mechanical robustness, and electrical/optical tunability. Such attributes unattainable with traditional electronic materials are particularly promising for high-performance artificial neurons and synapses, enabling energy-efficient operation, high integration density, and excellent scalability.

Thickness-Independent Semiconducting-to-Metallic Conversion in Wafer-Scale Two-Dimensional PtSe Layers by Plasma-Driven Chalcogen Defect Engineering.

Platinum diselenide (PtSe) is an emerging class of two-dimensional (2D) transition-metal dichalcogenide (TMD) crystals recently gaining substantial interest, owing to its extraordinary properties absent in conventional 2D TMD layers. Most interestingly, it exhibits a thickness-dependent semiconducting-to-metallic transition, i.e.

Ultrasensitive and ultrathin phototransistors and photonic synapses using perovskite quantum dots grown from graphene lattice.

Organic-inorganic halide perovskite quantum dots (PQDs) constitute an attractive class of materials for many optoelectronic applications. However, their charge transport properties are inferior to materials like graphene. On the other hand, the charge generation efficiency of graphene is too low to be used in many optoelectronic applications.

Two-Dimensional/Three-Dimensional Schottky Junction Photovoltaic Devices Realized by the Direct CVD Growth of vdW 2D PtSe Layers on Silicon.

Two-dimensional (2D) platinum diselenide (PtSe) layers are a new class of near-atom-thick 2D crystals in a van der Waals-assembled structure similar to previously explored many other 2D transition-metal dichalcogenides (2D TMDs). They exhibit distinct advantages over conventional 2D TMDs for electronics and optoelectronics applications such as metallic-to-semiconducting transition, decently high carrier mobility, and low growth temperature. Despite such superiority, much of their electrical properties have remained mostly unexplored, leaving their full technological potential far from being realized.

Artificial Neuron using Vertical MoS/Graphene Threshold Switching Memristors.

With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal-oxide-semiconductor (CMOS) circuitry in the past.

Improved performance of GaAsSb/AlGaAs nanowire ensemble Schottky barrier based photodetector via in situ annealing.

In this work, we report on the p-i GaAsSb/AlGaAs nanowires (NWs) ensemble device exhibiting good spectral response up to 1.1 μm with a high responsivity of 311 A W, an external quantum efficiency of 6.1 × 10%, and a detectivity of 1.