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-1, an external quantum efficiency of 6.1 × 104%, and a detectivity of 1.9 × 1010 Jones at 633 nm. The high responsivity of the NWs has been attributed to in situ post-growth annealing of GaAsSb axial NWs in the ultra-high vacuum. The enabling growth technology is molecular beam epitaxy for the Ga-assisted epitaxial growth of these NWs on Si (111) substrates. Room temperature Raman spectra, as well as temperature dependent micro-photoluminescence peak analysis indicated suppression of band tail states and non-radiative channels due to annealing. A similar improvement in in situ annealed p-i GaAsSb NW ensemble with an AlGaAs passivating shell was inferred from a reduction in the Schottky barrier height as well as the NW resistance compared to the as-grown NW ensemble. These results demonstrate in situ annealing of nanowires to be an effective pathway for improving the optoelectronic properties of the NWs and the device thereof.

MoS2 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. In this work, we demonstrate ultra-low-variability synapses using chemical vapor deposited 2D MoS2 as the switching medium with Ti/Au electrodes. These devices, fabricated using a transfer-free process, exhibit ultra-low variability in SET voltage, RESET power distribution, and synaptic weight update characteristics. This ultra-low variability is enabled by the interface rendered by a Ti/Au top contact on Si-rich MoS2 layers of mixed orientation, corroborated by transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), and X-ray photoelectron spectroscopy (XPS). TEM images further confirm the stability of the device stack even after subjecting the device to 100 SET-RESET cycles. Additionally, we implement logic gates by monolithic integration of MoS2 synapses with MoS2 leaky integrate-and-fire neurons to show the viability of these devices for non-von Neumann computing.

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. Ultraviolet-visible wavelength-sensitive MoS2 FET channel with infrared sensitive PtTe2/Si gate electrode enables the device to sense, store, and process optical data for a wide range of the electromagnetic spectrum, while maintaining a low dark current. The device exhibits optical stimulation-controlled short-term and long-term potentiation, electrically driven long-term depression, synaptic weight update for multiple wavelengths of light ranging from 300 nm in ultraviolet to 2 µm in infrared. An artificial neural network developed using the extracted weight update parameters of the device can be trained to identify both single wavelength and mixed wavelength patterns. This work demonstrates a device that could potentially be used for realizing a multiwavelength neuromorphic visual system for pattern recognition and object identification.

Optoelectronic synapse using monolayer MoS2 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. In this work, the trapping and de-trapping of photogenerated carriers in the MoS2/SiO2 interface of a n-channel MoS2 transistor was employed to emulate the optoelectronic synapse characteristics. The monolayer MoS2 field effect transistor (FET) exhibits photo-induced short-term and long-term potentiation, electrically driven long-term depression, paired pulse facilitation (PPF), spike time dependent plasticity, which are necessary synaptic characteristics. Moreover, the device's ability to retain its conductance state can be modulated by the gate voltage, making the device behave as a photodetector for positive gate voltages and an optoelectronic synapse at negative gate voltages.

Thickness-Independent Semiconducting-to-Metallic Conversion in Wafer-Scale Two-Dimensional PtSe2 Layers by Plasma-Driven Chalcogen Defect Engineering.

Platinum diselenide (PtSe2) 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., thick 2D PtSe2 layers, which are intrinsically metallic, become semiconducting with their thickness reduced below a certain point. Realizing both semiconducting and metallic phases within identical 2D PtSe2 layers in a spatially well-controlled manner offers unprecedented opportunities toward atomically thin tailored electronic junctions, unattainable with conventional materials. In this study, beyond this thickness-dependent intrinsic semiconducting-to-metallic transition of 2D PtSe2 layers, we demonstrate that controlled plasma irradiation can "externally" achieve such tunable carrier transports. We grew wafer-scale very thin (a few nm) 2D PtSe2 layers by a chemical vapor deposition (CVD) method and confirmed their intrinsic semiconducting properties. We then irradiated the material with argon (Ar) plasma, which was intended to make it more semiconducting by thickness reduction. Surprisingly, we discovered a reversed transition of semiconducting to metallic, which is opposite to the prediction concerning their intrinsic thickness-dependent carrier transports. Through extensive structural and chemical characterization, we identified that the plasma irradiation introduces a large concentration of near-atomic defects and selenium (Se) vacancies in initially stoichiometric 2D PtSe2 layers. Furthermore, we performed density functional theory (DFT) calculations and clarified that the band-gap energy of such defective 2D PtSe2 layers gradually decreases with increasing defect concentration and dimensions, accompanying a large number of midgap energy states. This corroborative experimental and theoretical study decisively verifies the fundamental mechanism for this externally controlled semiconducting-to-metallic transition in large-area CVD-grown 2D PtSe2 layers, greatly broadening their versatility for futuristic electronics.

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. Here, we demonstrate the development of ultrathin phototransistors and photonic synapses using a graphene-PQD (G-PQD) superstructure prepared by growing PQDs directly from a graphene lattice. We show that the G-PQDs superstructure synchronizes efficient charge generation and transport on a single platform. G-PQD phototransistors exhibit excellent responsivity of 1.4 × 108 AW-1 and specific detectivity of 4.72 × 1015 Jones at 430 nm. Moreover, the light-assisted memory effect of these superstructures enables photonic synaptic behavior, where neuromorphic computing is demonstrated by facial recognition with the assistance of machine learning. We anticipate that the G-PQD superstructures will bolster new directions in the development of highly efficient optoelectronic devices.

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. In this review, diverse 2D materials explored for neuromorphic applications, including graphene, transition metal dichalcogenides, hexagonal boron nitride, and black phosphorous, are comprehensively overviewed. Their promise for neuromorphic applications are fully discussed in terms of material property suitability and device operation principles. Furthermore, up-to-date demonstrations of neuromorphic devices based on 2D materials or their heterostructures are presented. Lastly, the challenges associated with the successful implementation of 2D materials into large-scale devices and their material quality control will be outlined along with the future prospect of these emergent materials.

Two-Dimensional/Three-Dimensional Schottky Junction Photovoltaic Devices Realized by the Direct CVD Growth of vdW 2D PtSe2 Layers on Silicon.

Two-dimensional (2D) platinum diselenide (PtSe2) 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. Herein, we report 2D/three-dimensional Schottky junction devices based on vertically aligned metallic 2D PtSe2 layers integrated on Si wafers. We directly grew 2D PtSe2 layers of controlled orientation and carrier transport characteristics via a low-temperature chemical vapor deposition process and investigated 2D PtSe2/Si Schottky junction properties. We unveiled a comprehensive set of material parameters, which decisively confirm the presence of excellent Schottky junctions, i.e., high-current rectification, small ideality factor, and temperature-dependent variation of Schottky barrier heights. Moreover, we observed strong photovoltaic effects in the 2D PtSe2/Si Schottky junction devices and extended them to realize flexible photovoltaic devices. This study is believed to significantly broaden the versatility of 2D PtSe2 layers in practical and futuristic electronic devices.

Artificial Neuron using Vertical MoS2/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. Recently, metal-insulator-transition materials have been used to realize artificial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS2/graphene van der Waals heterojunction system to produce the integrate-and-fire response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS2, enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-firing refractory period of a neuron and strength modulated frequency response. These results show that the developed artificial neuron can play a crucial role in neuromorphic computing.