Focused Surface Acoustic Wave induced nano-oscillator based reservoir computing

Abstract

We demonstrate using micromagnetic simulations that a nanomagnet array excited by Surface Acoustic Waves (SAWs) can work as a reservoir that can classify sine and square waves with high accuracy. To evaluate memory effect and computing capability, we study the Short-Term Memory (STM) and Parity Check (PC) capacities respectively. The simulated nanomagnet array has an input nanomagnet that is excited with focused SAW and coupled to several nanomagnets, seven of which serve as output nanomagnets. The SAW has a carrier frequency of 4 GHz whose amplitude is modulated to provide different inputs of sine and square waves of 100 MHz frequency. The responses of the selected output nanomagnets are processed by reading the envelope of their magnetization state, which is used to train the output weights using regression method (e.g. Moore-Penrose pseudoinverse operation). For classification, a random sequence of 100 square and sine wave samples are used, of which 80 % are used for training, and the rest of the samples used for testing. We achieve 100 % training accuracy and 100 % testing accuracy for different combination of nanomagnets as outputs. Further, the STM and PC is calculated to be ~ 5.5 bits and ~ 5.3 bits respectively, which is indicative of the proposed acoustically driven nanomagnet oscillator array being well suited for physical reservoir computing applications. Finally, the ability to use high frequency (4GHz, wavelength ~1 micron) SAW makes the device scalable to small dimensions, while the ability to modulate the envelope at lower frequency (100 MHz) adds flexibility to encode different signals beyond the sine and square waves demonstrated here.

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