Thermal noise is typically regarded as a nuisance when designing new device technologies. More so in nanomagnetic applications where it can lead to degraded device performance through device volatility and memory loss. In precise circumstances, however, the random variability guaranteed by thermal effects can be harnessed to enable useful device behavior at high energy efficiency and low power consumption. Thermal assistance has been known to guide phenomena such as brownian ratchets and stochastic resonance. From a dynamical systems perspective, this takes place when the physical system being studied either lacks detailed balance or is tuned periodically on timescales comparable with thermal fluctuation frequencies.
My research focuses both on modelling the thermal phenomena affecting spintronically driven nanomagnet dynamics and assessing how it can be controlled in the design of magnetic structures to implement novel computational tasks. As an example, the scalability and easy tunability of magnetic heterostructures make them ideal candidates for constructing dense ensembles of magnetically and electrically coupled devices. Similarly, stable magnetic solitons living on extended thin films (such as skyrmions) can serve as extremely compact nonlinear elements. Both of these properties are fundamental requirements for future analog devices capable of enacting the operating principles of neural networks and reservoir computers. My work uses both numerical micromagnetic techniques and theoretical insight to grapple underlying principles of the phenomenas I study and implement valid proof-of-concepts for real world applications.