Highly reproducible and CMOS-compatible VO2-based oscillators for brain-inspired computing.

With remarkable electrical and optical switching properties induced at low power and near room temperature (68 °C), vanadium dioxide (VO2) has sparked rising interest in unconventional computing among the phase-change materials research community. The scalability and the potential to compute beyond the von Neumann model make VO2 especially appealing for implementation in oscillating neural networks for artificial intelligence applications, to solve constraint satisfaction problems, and for pattern recognition. Its integration into large networks of oscillators on a Silicon platform still poses challenges associated with the stabilization in the correct oxidation state and the ability to fabricate a structure with predictable electrical behavior showing very low variability. In this work, the role played by the different annealing parameters applied by three methods (slow thermal annealing, flash annealing, and rapid thermal annealing), following the vanadium oxide atomic layer deposition, on the formation of VO2 grains is studied and an optimal substrate stack configuration that minimizes variability between devices is proposed. Material and electrical characterizations are performed on the different films and a step-by-step recipe to build reproducible VO2-based oscillators is presented, which is argued to be made possible thanks to the introduction of a hafnium oxide (HfO2) layer between the silicon substrate and the vanadium oxide layer. Up to seven nearly identical VO2-based devices are contacted simultaneously to create a network of oscillators, paving the way for large-scale implementation of VO2 oscillating neural networks.

A CMOS-compatible oscillation-based VO2 Ising machine solver.

Phase-encoded oscillating neural networks offer compelling advantages over metal-oxide-semiconductor-based technology for tackling complex optimization problems, with promising potential for ultralow power consumption and exceptionally rapid computational performance. In this work, we investigate the ability of these networks to solve optimization problems belonging to the nondeterministic polynomial time complexity class using nanoscale vanadium-dioxide-based oscillators integrated onto a Silicon platform. Specifically, we demonstrate how the dynamic behavior of coupled vanadium dioxide devices can effectively solve combinatorial optimization problems, including Graph Coloring, Max-cut, and Max-3SAT problems. The electrical mappings of these problems are derived from the equivalent Ising Hamiltonian formulation to design circuits with up to nine crossbar vanadium dioxide oscillators. Using sub-harmonic injection locking techniques, we binarize the solution space provided by the oscillators and demonstrate that graphs with high connection density (η > 0.4) converge more easily towards the optimal solution due to the small spectral radius of the problem's equivalent adjacency matrix. Our findings indicate that these systems achieve stability within 25 oscillation cycles and exhibit power efficiency and potential for scaling that surpasses available commercial options and other technologies under study. These results pave the way for accelerated parallel computing enabled by large-scale networks of interconnected oscillators.

Role of ambient temperature in modulation of behavior of vanadium dioxide volatile memristors and oscillators for neuromorphic applications.

Volatile memristors are versatile devices whose operating mechanism is based on an abrupt and volatile change of resistivity. This switching between high and low resistance states is at the base of cutting edge technological implementations such as neural/synaptic devices or random number generators. A detailed understanding of this operating mechanisms is essential prerequisite to exploit the full potentiality of volatile memristors. In this respect, multi-physics device simulations provide a powerful tool to single out material properties and device features that are the keys to achieve desired behaviors. In this paper, we perform 3D electrothermal simulations of volatile memristors based on vanadium dioxide (VO[Formula: see text]) to accurately investigate the interplay among Joule effect, heat dissipation and the external temperature [Formula: see text] over their resistive switching mechanism. In particular, we extract from our simulations a simplified model for the effect of [Formula: see text] over the negative differential resistance (NDR) region of such devices. The NDR of VO[Formula: see text] devices is pivotal for building VO[Formula: see text] oscillators, which have been recently shown to be essential elements of oscillatory neural networks (ONNs). ONNs are innovative neuromorphic circuits that harness oscillators' phases to compute. Our simulations quantify the impact of [Formula: see text] over figures of merit of VO[Formula: see text] oscillator, such as frequency, voltage amplitude and average power per cycle. Our findings shed light over the interlinked thermal and electrical behavior of VO[Formula: see text] volatile memristors and oscillators, and provide a roadmap for the development of ONN technology.

How Frequency Injection Locking Can Train Oscillatory Neural Networks to Compute in Phase.

Brain-inspired computing employs devices and architectures that emulate biological functions for more adaptive and energy-efficient systems. Oscillatory neural networks (ONNs) are an alternative approach in emulating biological functions of the human brain and are suitable for solving large and complex associative problems. In this work, we investigate the dynamics of coupled oscillators to implement such ONNs. By harnessing the complex dynamics of coupled oscillatory systems, we forge a novel computation model-information is encoded in the phase of oscillations. Coupled interconnected oscillators can exhibit various behaviors due to the strength of the coupling. In this article, we present a novel method based on subharmonic injection locking (SHIL) for controlling the oscillatory states of coupled oscillators that allow them to lock in frequency with distinct phase differences. Circuit-level simulation results indicate SHIL effectiveness and its applicability to large-scale oscillatory networks for pattern recognition.

Oscillatory Neural Networks Using VO2 Based Phase Encoded Logic.

Nano-oscillators based on phase-transition materials are being explored for the implementation of different non-conventional computing paradigms. In particular, vanadium dioxide (VO2) devices are used to design autonomous non-linear oscillators from which oscillatory neural networks (ONNs) can be developed. In this work, we propose a new architecture for ONNs in which sub-harmonic injection locking (SHIL) is exploited to ensure that the phase information encoded in each neuron can only take two values. In this sense, the implementation of ONNs from neurons that inherently encode information with two-phase values has advantages in terms of robustness and tolerance to variability present in VO2 devices. Unlike conventional interconnection schemes, in which the sign of the weights is coded in the value of the resistances, in our proposal the negative (positive) weights are coded using static inverting (non-inverting) logic at the output of the oscillator. The operation of the proposed architecture is shown for pattern recognition applications.

Coupled VO2 Oscillators Circuit as Analog First Layer Filter in Convolutional Neural Networks.

In this work we present an in-memory computing platform based on coupled VO2 oscillators fabricated in a crossbar configuration on silicon. Compared to existing platforms, the crossbar configuration promises significant improvements in terms of area density and oscillation frequency. Further, the crossbar devices exhibit low variability and extended reliability, hence, enabling experiments on 4-coupled oscillator. We demonstrate the neuromorphic computing capabilities using the phase relation of the oscillators. As an application, we propose to replace digital filtering operation in a convolutional neural network with oscillating circuits. The concept is tested with a VGG13 architecture on the MNIST dataset, achieving performances of 95% in the recognition task.

Ballistic One-Dimensional InAs Nanowire Cross-Junction Interconnects.

Coherent interconnection of quantum bits remains an ongoing challenge in quantum information technology. Envisioned hardware to achieve this goal is based on semiconductor nanowire (NW) circuits, comprising individual NW devices that are linked through ballistic interconnects. However, maintaining the sensitive ballistic conduction and confinement conditions across NW intersections is a nontrivial problem. Here, we go beyond the characterization of a single NW device and demonstrate ballistic one-dimensional (1D) quantum transport in InAs NW cross-junctions, monolithically integrated on Si. Characteristic 1D conductance plateaus are resolved in field-effect measurements across up to four NW-junctions in series. The 1D ballistic transport and sub-band splitting is preserved for both crossing-directions. We show that the 1D modes of a single injection terminal can be distributed into multiple NW branches. We believe that NW cross-junctions are well-suited as cross-directional communication links for the reliable transfer of quantum information as required for quantum computational systems.

Heat transport through atomic contacts.

Heat transport and dissipation at the nanoscale severely limit the scaling of high-performance electronic devices and circuits. Metallic atomic junctions serve as model systems to probe electrical and thermal transport down to the atomic level as well as quantum effects that occur in one-dimensional (1D) systems. Whereas charge transport in atomic junctions has been studied intensively in the past two decades, heat transport remains poorly characterized because it requires the combination of a high sensitivity to small heat fluxes and the formation of stable atomic contacts. Here we report heat-transfer measurements through atomic junctions and analyse the thermal conductance of single-atom gold contacts at room temperature. Simultaneous measurements of charge and heat transport reveal the proportionality of electrical and thermal conductance, quantized with the respective conductance quanta. This constitutes a verification of the Wiedemann-Franz law at the atomic scale.

Tuning the light emission from GaAs nanowires over 290 meV with uniaxial strain.

Strain engineering has been used to increase the charge carrier mobility of complementary metal-oxide-semiconductor transistors as well as to boost and tune the performance of optoelectronic devices, enabling wavelength tuning, polarization selectivity and suppression of temperature drifts. Semiconducting nanowires benefit from enhanced mechanical properties, such as increased yield strength, that turn out to be beneficial to amplify strain effects. Here we use photoluminescence (PL) to study the effect of uniaxial stress on the electronic properties of GaAs/Al0.3Ga0.7As/GaAs core/shell nanowires. Both compressive and tensile mechanical stress were applied continuously and reversibly to the nanowire, resulting in a remarkable decrease of the bandgap of up to 296 meV at 3.5% of strain. Raman spectra were measured and analyzed to determine the axial strain in the nanowire and the Poisson ratio in the <111> direction. In both PL and Raman spectra, we observe fingerprints of symmetry breaking due to anisotropic deformation of the nanowire. The shifts observed in the PL and Raman spectra are well described by bulk deformation potentials for band structure and phonon energies. The fact that exceptionally high elastic strain can be applied to semiconducting nanowires makes them ideally suited for novel device applications that require a tuning of the band structure over a broad range.

In situ doping of catalyst-free InAs nanowires.

We report on in situ doping of InAs nanowires grown by metal-organic vapor-phase epitaxy without any catalyst particles. The effects of various dopant precursors (Si(2)H(6), H(2)S, DETe, CBr(4)) on the nanowire morphology and the axial and radial growth rates are investigated to select dopants that enable control of the conductivity in a broad range and that concomitantly lead to favorable nanowire growth. In addition, the resistivity of individual wires was measured for different gas-phase concentrations of the dopants selected, and the doping density and mobility were extracted. We find that by using Si(2)H(6) axially and radially uniform doping densities up to 7 × 10(19) cm(-3) can be obtained without affecting the morphology or growth rates. For sulfur-doped InAs nanowires, we find that the distribution coefficient depends on the growth conditions, making S doping more difficult to control than Si doping. Moreover, above a critical sulfur gas-phase concentration, compensation takes place, limiting the maximum doping level to 2 × 10(19) cm(-3). Finally, we extract the specific contact resistivity as a function of doping concentration for Ti and Ni contacts.

Mapping active dopants in single silicon nanowires using off-axis electron holography.

We demonstrate that state-of-the-art off-axis electron holography can be used to map active dopants in silicon nanowires as thin as 60 nm with 10 nm spatial resolution. Experiment and simulation demonstrate that doping concentrations of 10(19) and 10(20) cm(-3) can be measured with a detection threshold of 10(18) cm(-3) with respect to intrinsic silicon. Comparison of experimental data and simulations allows an estimation of the charge density at the wire-oxide interface of -1 x 10(12) electron charges cm(-2). Off-axis electron holography thus offers unique capabilities for a detailed analysis of active dopant concentrations in nanostructures.

Doping limits of grown in situ doped silicon nanowires using phosphine.

Structural characterization and electrical measurements of silicon nanowires (SiNWs) synthesized by Au catalyzed vapor-liquid-solid growth using silane and axially doped in situ with phosphine are reported. We demonstrate that highly n-doped SiNWs can be grown without structural defects and high selectivity and find that addition of the dopant reduces the growth rate by less than 8% irrespective of the radius. This indicates that also the dopant incorporation is radius-independent. On the basis of electrical measurements on individual wires, contact resistivities as low as 1.2 x 10(-7) omega cm(-2) were extracted. Resistivity measurements reveal a reproducible donor incorporation of up to 1.5 x 1020 cm-3 using a gas phase ratios of Si/P = 1.5 x 10(-2). Higher dopant gas concentrations did not lead to an increase of the doping concentration beyond 1.5 x10(20) cm(-3).