Reservoir Computing with Connected Magnetic Nanoring Ensembles

The concept of in-materia computing uses the natural complexity of material systems to perform computational operations naturally as part of the system’s inherent response to input stimuli. However, to implement a material system for computation effectively, the physical response of the system must be understood and exploited under a suitable computational framework. This thesis explores the application of arrays of interconnected magnetic nanorings for computation under the framework of reservoir computing. By using a combination of experimental and simulation techniques, the work presented here aims to explore and understand the response of the nanoring arrays, exploit their interesting dynamic properties for computation, and expand upon the computational power achievable with the system. Firstly, the implementation of a phenomenological model of the nanoring arrays is described, then validated against a range of experimental data covering the static, dynamic, and microstate response of the nanoring arrays with good agreement. This model then serves as a testbed for establishing a suitable paradigm for computing with the nanorings and exploring the computational properties of different regimes of response, ending with a proof-of-concept demonstration of reservoir computing with the nanorings on a benchmark spoken digit recognition task. Next, the findings made in simulation are used to inform the development of an experimental demonstration of computation. This involved the creation of experimental apparatus to apply stimuli to the nanorings via rotating magnetic fields, and to measure the evolving anisotropic magnetoresistance of the device. Interesting dynamic properties of the system’s resistance response are identified and paired with specific reservoir architectures that leverages them to provide different computational properties, evidenced by state-of-the art performances in a range of standard tasks. Finally, the changes in physical behaviour due to manipulations of the array’s lattice structure are explored at the microstate level as well as their macroscale response. Computational properties of the different arrangements are evaluated, and lack of microstate resolution in the readout mechanism is attributed to the subtlety of the differences. However, the additional computational power available when different arrangements are combined shows promising scalability for devices of the nanoring arrays.