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Register a new userExperimental demonstration of a rapid sweep-tuned spectrum analyzer with temporal resolution based on a spin-torque nano-oscillator
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It is demonstrated experimentally that a spin-torque nano-oscillator (STNO) rapidly sweep-tuned by a bias voltage can be used for time-resolved spectrum analysis of frequency-manipulated microwave signals with complicated multi-tone spectra. The critical reduction in the time of spectrum analysis comes from the naturally small time constants of a nano-sized STNO (1-100 ns). The demonstration is performed on a vortex-state STNO generating in a frequency range around 300 MHz, with frequency down-conversion and matched filtering used for signal processing. It is shown that this STNO-based spectrum analyzer can perform analysis of multi-tone signals, and signals with rapidly changing frequency components with time resolution on a $mu$s time scale and frequency resolution limited only by the bandwidth theorem. The proposed concept of rapid time-resolved spectrum analysis can be implemented with any type of micro and nano-scale frequency-swept oscillators having low time constants and high oscillation frequency.
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A spintronic method of ultra-fast broadband microwave spectrum analysis is proposed. It uses a rapidly tuned spin torque nano-oscillator (STNO), and does not require injection locking. This method treats an STNO generating a microwave signal as an element with an oscillating resistance. When an external signal is applied to this resistor for analysis, it is mixed with the signal generated by the STNO. The resulting mixed voltage contains the sum and difference frequencies, and the latter produces a DC component when the external frequency matches the frequency generated by the STNO. The mixed voltage is processed using a low pass filter to exclude the sum frequency components, and a matched filter to exclude the dependence of the resultant DC voltage on the phase difference between the two signals. It is found analytically and by numerical simulation, that the proposed spectrum analyzer has a frequency resolution at a theoretical limit in a real-time scanning bandwidth of 10~GHz, and a frequency scanning rate above 1~GHz/ns, while remaining sensitive to signal power as low as the Johnson-Nyquist thermal noise floor.
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.
We use He$^+$ irradiation to tune the nonlinearity, $mathcal{N}$, of all-perpendicular spin-torque nano-oscillators (STNOs) using the He$^+$ fluence-dependent perpendicular magnetic anisotropy (PMA) of the [Co/Ni] free layer. Employing fluences from 6 to 20$times10^{14}$~He$^{+}$/cm$^{2}$, we are able to tune $mathcal{N}$ in an in-plane field from strongly positive to moderately negative. As the STNO microwave signal properties are mainly governed by $mathcal{N}$, we can in this way directly control the threshold current, the current tunability of the frequency, and the STNO linewidth. In particular, we can dramatically improve the latter by more than two orders of magnitude. Our results are in good agreement with the theory for nonlinear auto-oscillators, confirm theoretical predictions of the role of nonlinearity, and demonstrate a straightforward path towards improving the microwave properties of STNOs.
Action potentials are the basic unit of information in the nervous system and their reliable detection and decoding holds the key to understanding how the brain generates complex thought and behavior. Transducing these signals into microwave field oscillations can enable wireless sensors that report on brain activity through magnetic induction. In the present work we demonstrate that action potentials from crayfish lateral giant neuron can trigger microwave oscillations in spin-torque nano-oscillators. These nanoscale devices take as input small currents and convert them to microwave current oscillations that can wirelessly broadcast neuronal activity, opening up the possibility for compact neuro-sensors. We show that action potentials activate microwave oscillations in spin-torque nano-oscillators with an amplitude that follows the action potential signal, demonstrating that the device has both the sensitivity and temporal resolution to respond to action potentials from a single neuron. The activation of magnetic oscillations by action potentials, together with the small footprint and the high frequency tunability, makes these devices promising candidates for high resolution sensing of bioelectric signals from neural tissues. These device attributes may be useful for design of high-throughput bi-directional brain-machine interfaces.
A theoretical study of delayed feedback in a spin-torque nano-oscillator model is presented. The feedback acts as a modulation of the supercriticality, which results in changes in the oscillator frequency through a strong nonlinearity, amplitude modulations, and a rich modulation sideband structure in the power spectrum at long delays. Modulation sidebands persist at finite temperatures but some of the complex structure is lost through the finite coherence time of the oscillations.
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