Focused Surface Acoustic Wave induced nano-oscillator based reservoir computing
Md. Fahim F. Chowdhury, Walid Al Misba, Md Mahadi Rajib, Alexander J., Edwards, Dhritiman Bhattacharya, Joseph S. Friedman, Jayasimha Atulasimha

TL;DR
This paper demonstrates that a nanomagnet array excited by focused Surface Acoustic Waves can serve as an effective physical reservoir for classifying waveforms with high accuracy, leveraging high-frequency SAWs for scalability.
Contribution
It introduces a novel SAW-driven nanomagnet reservoir computing system capable of high-accuracy waveform classification and evaluates its memory and computational capacities.
Findings
Achieved 100% training and testing accuracy in waveform classification.
Demonstrated high short-term memory (~5.5 bits) and parity check (~5.3 bits) capacities.
Scalable design using high-frequency (4 GHz) SAWs for small device dimensions.
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…
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