Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS 2 /SiO x /Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS 2 -based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS 2 /SiO x /Ni synapses, when integrated with a MoS 2 -based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS 2 as a viable platform for all-memristive SNNs.

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.

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.

The semiconductor-to-metal transition of vanadium dioxide (VO2) films is studied using temperature-dependent Raman, optical, and electrical measurements. The VO2 films are deposited via an atomic layer deposition (ALD) process using alternate pulses of vanadium tetrachloride and H2O at 350 °C. A growth rate of 0.021 nm/cycle and a thickness of 33 nm of VO2 are obtained for all films studied. The phase of the film is determined using x-ray diffraction. The as-deposited films are amorphous and are transformed to the monoclinic phase with a post-deposition, forming gas anneal at temperatures ≥ 500 °C for 60 min. The purity of the films is determined using x-ray photoelectron spectroscopy and no evidence of residual chlorine is detected. The temperature-dependent Raman Ag mode of the monoclinic VO2 phase is observed to monotonically decrease from 25 °C to 78 °C; where no evidence of the Ag peak is observed in the film beyond 68 °C. The refractive index and extinction coefficient extracted from temperature-dependent ellipsometry confirm that, beyond 68 °C, free carriers are generated in the film. Electrical measurements performed on a fabricated p++Si/VO2/Ti/Au device show a semiconductor-to-metal transition behavior with a high resistance of 14701 ± 2284 Ω at 62 °C and a low resistance of 1064.1 ± 143 Ω at 67 °C. This work demonstrates that a halide-based ALD process provides a clean and robust approach to synthesizing high-quality VO2 films.

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.

An extremely thin superstructure is developed for applications in phototransistors and neuromorphic computers.

An artificial nociceptor realized with a single 2D MoS 2 -based memristor device is demonstrated in this work. The threshold switching memristor (TSM) device exhibits volatile resistance switching characteristics with low threshold voltage and a high ON-OFF ratio of 10 6 . The Au/MoS 2 /Ag TSM device imitates a nociceptor, a special receptor of a sensory neuron that can detect noxious stimulus and transfer the signal to the central nervous system for preventive actions. The single device exhibits all key features of nociceptors including threshold, relaxation, “no adaptation” and sensitization phenomena of allodynia and hyperalgesia depending on the strength, duration, and repetition of the external stimuli. This work indicates applicability of this device in artificial sensory alarm systems for humanoid robots.

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.

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.

In this work, we use a two-terminal 2D MoS2-based memristive device to emulate an artificial neuron. The Au/MoS2/Ag device exhibits volatile resistance switching characteristics with a low threshold voltage and a high ON-OFF ratio of 10(6), originating from an Ag diffusion-based filamentary process. The leaky integrate-and-fire neuron implemented with this device successfully emulates the key characteristics of a biological neuron.

Memristive synaptic devices are considered one of the most promising candidates for brain-inspired neuromorphic computing, owing to their decreased complexity and nanoscale footprint compared to conventional complementary metal oxide semiconductor (CMOS) circuitry[1], [2]. However, the non-linearity and asymmetry in weight update observed in most memristive synapses due to the difference in the long-term potentiation and depression characteristics makes it difficult to use these devices for unsupervised learning applications[1]. Previously, linearity in synaptic weight update has been engineered using non-identical input voltage pulsing scheme[3]. However, not many reports on linear synaptic weight update using identical input voltage pulses exist in literature [4]. In this work, we present large-area chemical vapor-deposited (CVD) MoS2 /graphene memristive synapses which exhibit linear weight update using identical input voltage pulses. These synaptic devices also exhibit spike-timing dependent plasticity, essential for online training.

Emulating the human brain's circuitry composed of neurons and synapses is an emerging area of research in mitigating the von Neumann bottleneck in present computer architectures. The building block of these neuromorphic systems-the synapse-is commonly realized with oxide-based or phase change material-based devices, whose operation is limited by high programming currents and high reset currents. In this work, we have realized nonvolatile resistive switching MoS2/graphene devices that exhibit multiple conductance states at low operating currents. The MoS2/graphene devices exhibit essential synaptic behaviors, such as short and long-term potentiation, long-term depression, and the spike timing dependent plasticity learning rule. Most importantly, they exhibit a near-linear synaptic weight update, without any abrupt reset process, allowing their use in unsupervised learning applications. These electronic synapses are built with chemical vapor deposited MoS2 and graphene, demonstrating potential for large-scale realizations of machine learning hardware.

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.

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-MoS 2 /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 MoS 2 , 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.

The phenomenal evolution of information and communication drives future technologies towards highly parallel, energy-efficient self-learning systems like the human brain. The limitations of current von Neumann computation systems have paved the way for artificial neural networks (ANN) to meet these criteria. The memristor has become an emerging candidate to realize ANN through emulating biological synapse and neuron behavior [1]–[3]. We previously reported an artificial neuron with 2D Mos2 and graphene electrode, but the operating voltage was high, and the output current was low [4]. In this work, we harness threshold switching in Mos2 enabled by Ag electrode, to emulate integration and firing behavior of neuron and demonstrate digit recognition application with these devices. The simple vertical structure of Ag/MoS2/ Au threshold switching memristor (TSM), with very low threshold voltage $(\mathrm{V}_{\mathrm{t}\mathrm{h}}=0.4-0.5\mathrm{V})$, displays the four crucial features of neuron─ all-or-nothing spiking, threshold-driven firing, post firing refractory period and stimulus strength based frequency response.

The total-ionizing-dose response of few layer MoS2 transistors with ZrO2 or h-BN gate dielectrics is investigated under various bias conditions. Defects in MoS2 and surrounding dielectric layers significantly affect radiation-induced trapping. For devices with ZrO2 dielectrics, much larger negative Vth shifts and peak transconductance degradation are observed for irradiation under negative and ground bias than under positive bias. The h-BN devices exhibit positive threshold voltage shifts under negative-bias irradiation. For both ZrO2 and h-BN passivated devices, the peak transconductance degradation results from charge trapping at the surface of the MoS2 or in nearby oxides. Changes in defect energy distributions of MoS2 FETs during X-ray irradiation are characterized via temperature-dependent low-frequency noise measurements. Density functional theory calculations are performed to provide insight into the pertinent defects.

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.

Two-dimensional materials provide a versatile platform for various electronic and optoelectronic devices, due to their uniform thickness and pristine surfaces. We probe the superior quality of 2D/2D and 2D/3D interfaces by fabricating molybdenum disulfide (MoS2)-based field effect transistors having hexagonal boron nitride (h-BN) and Al2O3 as the top gate dielectrics. An extremely low trap density of similar to 7 X 10(10) states/cm(2)-eV is extracted at the 2D/2D interfaces with h-BN as the top gate dielectric on the MoS2 channel. 2D/3D interfaces with Al2O3 as the top gate dielectric and SiOx as the nucleation layer exhibit trap densities between 7 X 10(10) and 10(11) states/cm(2) -eV, which are lower than previously reported 2D-channel/high-j-dielectric interface trap densities. The comparable values of trap time constants for both interfaces imply that similar types of defects contribute to the interface traps. This work establishes the case for van der Waals systems where the superior quality of 2D/2D and 2D/high-kappa dielectric interfaces can produce high performance electronic and optoelectronic devices. Published by AIP Publishing.

The performances of heterostructural devices are often limited by misfit dislocation. In this paper, a theoretical approach for misfit dislocation reduction of wurtzite InxGa1-xN/GaN is presented. The linear and exponential grading techniques have been modeled for the reduction of dislocation. An energy balance model has been taken into consideration and modified for wurtzite structure to evaluate the misfit dislocation density. The value of misfit dislocation has been reduced from 7.112×1010 cm-2 to 6.19×106 cm-2 and 7.039×1010 cm-2 to 6.121×106 cm-2 at the plane 1/3<> {} and 1/3<>{} respectively for linear grading. In case of exponential grading the dislocation density has been reduced to 2.762×105 cm-2 for both slip systems. Because of tapered grading coefficient a tapered dislocation profile has been reported in case of exponential grading technique. Finally, a comparative study has been shown among without graded, linear and exponential grading.