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.

Graphene-oxide interface for optoelectronic synapse application.

Optoelectronic synapses combine the functionalities of a non-volatile memory and photodetection in the same device, paving the path for the realization of artificial retina systems which can capture, pre-process, and identify images on the same platform. Graphene/Ta2O5/graphene phototransistor exhibits synapse characteristics when visible electromagnetic radiation of wavelength 405 nm illuminates the device. The photocurrent is retained after light withdrawal when positive gate voltage is applied to the device. The device exhibits distinct conductance states, modulated by different parameters of incident light, such as pulse width and number of pulses. The conductance state can be retained for 104 s, indicating long term potentiation (LTP), similar to biological synapses. By using optical and electrical pulses, the device shows optical potentiation and electrical LTD repeatably, implying their applicability in neural networks for pattern recognition.

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.

Graphene Enhances Actin Filament Assembly Kinetics and Modulates NIH-3T3 Fibroblast Cell Spreading.

Actin plays critical roles in various cellular functions, including cell morphogenesis, differentiation, and movement. The assembly of actin monomers into double-helical filaments is regulated in surrounding microenvironments. Graphene is an attractive nanomaterial that has been used in various biomaterial applications, such as drug delivery cargo and scaffold for cells, due to its unique physical and chemical properties. Although several studies have shown the potential effects of graphene on actin at the cellular level, the direct influence of graphene on actin filament dynamics has not been studied. Here, we investigate the effects of graphene on actin assembly kinetics using spectroscopy and total internal reflection fluorescence microscopy. We demonstrate that graphene enhances the rates of actin filament growth in a concentration-dependent manner. Furthermore, cell morphology and spreading are modulated in mouse embryo fibroblast NIH-3T3 cultured on a graphene surface without significantly affecting cell viability. Taken together, these results suggest that graphene may have a direct impact on actin cytoskeleton remodeling.

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.

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.

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.

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.

Centimeter-Scale Periodically Corrugated Few-Layer 2D MoS2 with Tensile Stretch-Driven Tunable Multifunctionalities.

Two-dimensional (2D) transition metal dichalcogenide (TMD) layers exhibit superior optical, electrical, and structural properties unattainable in any traditional materials. Many of these properties are known to be controllable via external mechanical inputs, benefiting from their extremely small thickness coupled with large in-plane strain limits. However, realization of such mechanically driven tunability often demands highly complicated engineering of 2D TMD layer structures, which is difficult to achieve on a large wafer scale in a controlled manner. Herein, we explore centimeter-scale periodically corrugated 2D TMDs, particularly 2D molybdenum disulfide (MoS2), and report their mechanically tunable multifunctionalities. We developed a water-assisted process to homogeneously integrate few layers of 2D MoS2 on three-dimensionally corrugated elastomeric substrates on a large area (>2 cm2). The evolution of electrical, optical, and structural properties in these three-dimensionally corrugated 2D MoS2 layers was systematically studied under controlled tensile stretch. We identified that they present excellent electrical conductivity and photoresponsiveness as well as systematically tunable surface wettability and optical absorbance even under significant mechanical deformation. These novel three-dimensionally structured 2D materials are believed to offer exciting opportunities for large-scale, mechanically deformable devices of various form factors and unprecedented multifunctionalities.