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.

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.

Scalable Van der Waals Two-Dimensional PtTe2 Layers Integrated onto Silicon for Efficient Near-to-Mid Infrared Photodetection.

In recent years, there has been increasing interest in leveraging two-dimensional (2D) van der Waals (vdW) crystals for infrared (IR) photodetection, exploiting their unusual optoelectrical properties. Some 2D vdW materials with small band gap energies such as graphene and black phosphorus have been explored as stand-alone IR responsive layers in photodetectors. However, the devices incorporating these IR-sensitive 2D layers often exhibited poor performances owing to their preparation issues such as limited scalability and air instability. Herein, we explored wafer-scale 2D platinum ditelluride (PtTe2) layers for near-to-mid IR photodetection by directly growing them onto silicon (Si) wafers. 2D PtTe2/Si heterojunctions exhibited wavelength- and intensity-dependent high photocurrents in a spectral range of ∼1-7 µm, significantly outperforming stand-alone 2D PtTe2 layers. The observed superiority is attributed to their excellent Schottky junction characteristics accompanying suppressed carrier recombination as well as optical absorbance competition between 2D PtTe2 layers and Si. The direct and scalable growth of 2D PtTe2 layers was further extended to demonstrate mechanically flexible IR photodetectors.

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.