  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.
