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Register a new userReservoir computing with the frequency, phase and amplitude of spin-torque nano-oscillators
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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.
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Spin torque nano-oscillators (STNO) are nanoscale devices with wide band frequency tunability. Their multifunctional RF properties are well suited to define novel schemes for wireless communications that use basic protocols for data transmission such as amplitude, frequency and phase shift keying (ASK, FSK, PSK). In contrast to ASK and FSK, implementation of PSK is more challenging for STNOs because of their relatively high phase noise. Here we introduce a special PSK technique by combining their modulation and injection locking functionality. The concept is validated using magnetic tunnel junction based vortex STNOs for injection locking at 2f and f/2 showing phase shifts up to 2.1rad and data transmission rates up to 4Mbit/s. Quadrature phase shift keying and analog phase modulation are also implemented, where the latter is employed for voice transmission over a distance of 10 meters. This demonstrates that STNO phase noise and output power meet the requested performances for operation in existing communication schemes.
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.
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 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.
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