Liquid ferrofluid synapses for spike-based neuromorphic learning

Abstract

Solid-state memory devices have emerged as promising synapses for neuromorphic engineering and computing. However, features such as limited endurance, static sensitivity, and lower ON/OFF ratios, as well as the need for peculiar conditions including current compliance and forming, still make their adoption challenging. Here we report a liquid state neuromorphic device based on a ferrofluid that exhibits short-term plasticity featuring extraordinary properties: a lower dynamic range, a high endurance, a fault tolerance capability, a deterministic resistance switching behavior, and no need for prerequisites such as a forming procedure and compliance current requirements. We also show how to stabilize nanoparticles using oleic acid as the surfactant, resulting in a yield increase and a smaller resistance variance. Additionally, we propose a low-power inference system on such a liquid synapse by applying the minimal magnitude of read biases, which are only affected to about 10% by the offset, gain errors, and noise of the system. Finally, we show the liquid synapse's feature to scale down the size and the capability to classify digits using a spike-based unsupervised learning method.