Distinguishing artificial spin ice states using magnetoresistance effect for neuromorphic computing

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing. Artificial spin ices consist of small magnets arranged in a lattice. Their simplicity belies their rich behaviour; they allowed for the investigation of effective magnetic monopoles, and more recently have been suggested as promising platforms for neuromorphic computing. For this latter function, efficient readout of the artificial spin ice state is critical. In this manuscript, Hu et al succeed in distinguishing artificial spin ice states using simple transport measurements.