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Can we build small neuromorphic chips capable of training deep networks with billions of parameters? This challenge requires hardware neurons and synapses with nanometric dimensions, which can be individually tuned, and densely connected. While nanosynaptic devices have been pursued actively in recent years, much less has been done on nanoscale artificial neurons. In this paper, we show that spintronic nano-oscillators are promising to implement analog hardware neurons that can be densely interconnected through electromagnetic signals. We show how spintronic oscillators maps the requirements of artificial neurons. We then show experimentally how an ensemble of four coupled oscillators can learn to classify all twelve American vowels, realizing the most complicated tasks performed by nanoscale neurons.
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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.
The harvesting of ambient radio-frequency (RF) energy is an attractive and clean way to realize the idea of self-powered electronics. Here we present a design for a microwave energy harvester based on a nanoscale spintronic diode (NSD). This diode contains a magnetic tunnel junction with a canted magnetization of the free layer, and can convert RF energy over the frequency range from 100 MHz to 1.2 GHz into DC electric voltage. An attractive property of the developed NSD is the generation of an almost constant DC voltage in a wide range of frequencies of the external RF signals. We further show that the developed NSD provides sufficient DC voltage to power a low-power nanodevice - a black phosphorus photo-sensor. Our results demonstrate that the developed NSD could pave the way for using spintronic detectors as building blocks for self-powered nano-systems, such as implantable biomedical devices, wireless sensors, and portable electronics.
Neurons in the brain behave as a network of coupled nonlinear oscillators processing information by rhythmic activity and interaction. Several technological approaches have been proposed that might enable mimicking the complex information processing of neuromorphic computing, some of them relying on nanoscale oscillators. For example, spin torque oscillators are promising building blocks for the realization of artificial high-density, low-power oscillatory networks (ON) for neuromorphic computing. The local external control and synchronization of the phase relation of oscillatory networks are among the key challenges for implementation with nanotechnologies. Here we propose a new method of phase programming in ONs by manipulation of the saturation magnetization, and consequently the resonance frequency of a single oscillator via Joule heating by a simple DC voltage input. We experimentally demonstrate this method in a pair of stray field coupled magnetic vortex oscillators. Since this method only relies on the oscillatory behavior of coupled oscillators, and the temperature dependence of the saturation magnetization, it allows for variable phase programming in a wide range of geometries and applications that can help advance the efforts of high frequency neuromorphic spintronics up to the GHz regime.
The emergence of exceptional points (EPs) in the parameter space of a (2D) eigenvalue problem is studied in a general sense in mathematical physics. In coupled systems, it gives rise to unique physical phenomena upon which novel approaches for the development of seminal types of highly sensitive sensors shall leverage their fascinating properties. Here, we demonstrate at room temperature the emergence of EPs in coupled spintronic nano-oscillators, coming along with the observation of amplitude death of self-oscillations and other complex dynamics. The main experimental features are properly described by the linearized theory of coupled system dynamics we develop. Interestingly, these spintronic nanoscale oscillators are deployment-ready in different applicational fields, such as sensors or radiofrequeny and wireless technology and, more recently, novel neuromorphic hardware solutions. Their unique and versatile properties, notably their large nonlinear behavior open up unprecedented perspectives in experiments as well as in theory on the physics of exceptional points.
The recent demonstration of neuromorphic computing with spin-torque nano-oscillators has opened a path to energy efficient data processing. The success of this demonstration hinged on the intrinsic short-term memory of the oscillators. In this study, we extend the memory of the spin-torque nano-oscillators through time-delayed feedback. We leverage this extrinsic memory to increase the efficiency of solving pattern recognition tasks that require memory to discriminate different inputs. The large tunability of these non-linear oscillators allows us to control and optimize the delayed feedback memory using different operating conditions of applied current and magnetic field.
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