Magnetic control of flexible thermoelectric devices based on macroscopic 3D interconnected nanowire networks

Spin-related effects in thermoelectricity can be used to design more efficient refrigerators and offer novel promising applications for the harvesting of thermal energy. The key challenge is to design structural and compositional magnetic material systems with sufficiently high efficiency and power output for transforming thermal energy into electric energy and vice versa. Here, the fabrication of large-area 3D interconnected Co/Cu nanowire networks is demonstrated, thereby enabling the controlled Peltier cooling of macroscopic electronic components with an external magnetic field. The flexible, macroscopic devices overcome inherent limitations of nanoscale magnetic structures due to insufficient power generation capability that limits the heat management applications. From properly designed experiments, large spin-dependent Seebeck and Peltier coefficients of $-9.4$ $\mu$V/K and $-2.8$ mV at room temperature, respectively. The resulting power factor of Co/Cu nanowire networks at room temperature ($\sim7.5$ mW/K$^2$m) is larger than those of state of the art thermoelectric materials, such as BiTe alloys and the magneto-power factor ratio reaches about 100\% over a wide temperature range. Validation of magnetic control of heat flow achieved by taking advantage of the spin-dependent thermoelectric properties of flexible macroscopic nanowire networks lay the groundwork to design shapeable thermoelectric coolers exploiting the spin degree of freedom.Comment: 11 pages, 7 figure

Neuromorphic spintronics simulated using an unconventional data-driven Thiele equation approach

In this study, we developed a quantitative description of the dynamics of spin-torque vortex nano-oscillators (STVOs) through an unconventional model based on the combination of the Thiele equation approach (TEA) and data from micromagnetic simulations (MMS). Solving the STVO dynamics with our analytical model allows to accelerate the simulations by 9 orders of magnitude compared to MMS while reaching the same level of accuracy. Here, we showcase our model by simulating a STVO-based neural network for solving a classification task. We assess its performance with respect to the input signal current intensity and the level of noise that might affect such a system. Our approach is promising for accelerating the design of STVO-based neuromorphic computing devices while decreasing drastically its computational cost.Comment: Presented in ISCS202

Skyrmion Gas Manipulation for Probabilistic Computing

The topologically protected magnetic spin configurations known as skyrmions offer promising applications due to their stability, mobility and localization. In this work, we emphasize how to leverage the thermally driven dynamics of an ensemble of such particles to perform computing tasks. We propose a device employing a skyrmion gas to reshuffle a random signal into an uncorrelated copy of itself. This is demonstrated by modelling the ensemble dynamics in a collective coordinate approach where skyrmion-skyrmion and skyrmion-boundary interactions are accounted for phenomenologically. Our numerical results are used to develop a proof-of-concept for an energy efficient ($\sim\mu\mathrm{W}$) device with a low area imprint ($\sim\mu\mathrm{m}^2$). Whereas its immediate application to stochastic computing circuit designs will be made apparent, we argue that its basic functionality, reminiscent of an integrate-and-fire neuron, qualifies it as a novel bio-inspired building block.Comment: 41 pages, 20 figure

Neuromorphic spintronics accelerated by an unconventional data-driven Thiele equation approach

A hardware neural network based on a single spin-torque vortex nano-oscillator is designed using time-multiplexing. The behavior of the spin-torque vortex nano-oscillator is simulated with an improved ultra-fast and quantitative model based on the Thiele equation approach. Different mathematical and numerical adaptations are brought to the model in order to increase the accuracy and the speed of the simulations. A benchmark task of waveform classification is designed to assess the performance of the neural network in the framework of reservoir computing and compare two different versions of the model. The obtained results allow to conclude on the ability of the system to effectively classify sine and square signals with high accuracy and low root-mean-square error, reflecting high confidence cognition. Given the high throughput of the simulations, two innovative parametric studies on the dc bias current intensity and the level of noise in the system are performed to demonstrate the value of our models. The efficiency of our system is also tested during speech recognition and shows the agreement between these models and the corresponding experimental measurements.Comment: 10 pages, 7 figure

Nonlinear behavior and mode coupling in spin transfer nano-oscillators

By investigating thoroughly the tunable behavior of coupled modes, we highlight how it provides new means to handle the properties of spin transfer nano-oscillators. We first demonstrate that the main features of the microwave signal associated with coupled vortex dynamics i.e. frequency, spectral coherence, critical current, mode localization, depends drastically on the relative vortex core polarities. Secondly we report a large reduction of the nonlinear linewidth broadening obtained by changing the effective damping through the control of the core configuration. Such a level of control on the nonlinear behavior reinforces our choice to exploit the microwave properties of collective modes for applications of spintronic devices in novel generation of integrated telecommunication devices

Reservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators

Spin-torque nano-oscillators can emulate neurons at the nanoscale. Recent works show that the non-linearity of their oscillation amplitude can be leveraged to achieve waveform classification for an input signal encoded in the amplitude of the input voltage. Here we show that the frequency and the phase of the oscillator can also be used to recognize waveforms. For this purpose, we phase-lock the oscillator to the input waveform, which carries information in its modulated frequency. In this way we considerably decrease amplitude, phase and frequency noise. We show that this method allows classifying sine and square waveforms with an accuracy above 99% when decoding the output from the oscillator amplitude, phase or frequency. We find that recognition rates are directly related to the noise and non-linearity of each variable. These results prove that spin-torque nano-oscillators offer an interesting platform to implement different computing schemes leveraging their rich dynamical features

Memristive and tunneling effects in 3D interconnected silver nanowires

Due to their memristive properties nanowire networks are very promising for neuromorphic computing applications. Indeed, the resistance of such systems can evolve with the input voltage or current as it confers a synaptic behaviour to the device. Here, we propose a network of silver nanowires (Ag-NWs) which are grown in a nanopourous membrane with interconnected nanopores by electrodeposition. This bottom-up approach fabrication method gives a conducting network with a 3D architecture and a high density of Ag-NWs. The resulting 3D interconnected Ag-NW network exhibits a high initial resistance as well as a memristive behavior. It is expected to arise from the creation and the destruction of conducting silver filaments inside the Ag-NW network. Moreover, after several cycles of measurement, the resistance of the network switches from a high resistance regime, in the GOhm range, with a tunnel conduction to a low resistance regime, in the kOhm range.Comment: 8 pages, 5 figure

Efficacy and tolerability of a gatifloxacin/prednisolone acetate fixed combination for topical prophylaxis and control of inflammation in phacoemulsification: a 20-day-double-blind comparison to its individual components

OBJECTIVE: To compare the efficacy and tolerability of a fixed combination of 0.3% gatifloxacin and 1% prednisolone (Zypred®) versus the individual components used separately (Zypred® and Predfort®) for infection prophylaxis and inflammation control after cataract surgery with intraocular lens implantation. METHODS: A prospective, randomized, double-blind, parallel-group study of 108 patients who underwent phacoemulsification and intraocular lens implantation was conducted. After random assignment, 47 eyes received the fixed combination of topical 0.3% gatifloxacin/1% prednisolone drops, and 61 eyes received the same doses of the individual components as separate solutions four times a day for 15 days. Baseline and postoperative assessments were made on postoperative days 1, 7, 15, and 20. RESULTS: All objective (best corrected visual acuity, sign of active ocular inflammation, central and incisional corneal edema, the number of cells per high-power field in the anterior chamber, and intraocular pressure) and subjective (eye pain, photophobia, burning sensation, itching, and foreign body sensation) criteria of efficacy were similar in both groups, with no significant differences. Group I included 47 eyes that received the fixed combination of gatifloxacin/prednisolone acetate eye drops and a placebo eye drop solution. Group II included 61 eyes that were treated with 0.3% gatifloxacin and 1% prednisolone acetate eye drops separately. The intraocular pressure was slightly higher in Group II (

Designing large arrays of interacting spin-torque nano-oscillators for microwave information processing

Arrays of spin-torque nano-oscillators are promising for broadband microwave signal detection and processing, as well as for neuromorphic computing. In many of these applications, the oscillators should be engineered to have equally-spaced frequencies and equal sensitivity to microwave inputs. Here we design spin-torque nano-oscillator arrays with these rules and estimate their optimum size for a given sensitivity, as well as the frequency range that they cover. For this purpose, we explore analytically and numerically conditions to obtain vortex spin-torque nano-oscillators with equally-spaced gyrotropic oscillation frequencies and having all similar synchronization bandwidths to input microwave signals. We show that arrays of hundreds of oscillators covering ranges of several hundred MHz can be built taking into account nanofabrication constraints

Role of non-linear data processing on speech recognition task in the framework of reservoir computing

The reservoir computing neural network architecture is widely used to test hardware systems for neuromorphic computing. One of the preferred tasks for bench-marking such devices is automatic speech recognition. However, this task requires acoustic transformations from sound waveforms with varying amplitudes to frequency domain maps that can be seen as feature extraction techniques. Depending on the conversion method, these may obscure the contribution of the neuromorphic hardware to the overall speech recognition performance. Here, we quantify and separate the contributions of the acoustic transformations and the neuromorphic hardware to the speech recognition success rate. We show that the non-linearity in the acoustic transformation plays a critical role in feature extraction. We compute the gain in word success rate provided by a reservoir computing device compared to the acoustic transformation only, and show that it is an appropriate benchmark for comparing different hardware. Finally, we experimentally and numerically quantify the impact of the different acoustic transformations for neuromorphic hardware based on magnetic nano-oscillators.Comment: 13 pages, 5 figure