Liquid ferrofluid synapses for spike-based neuromorphic learning

Mohan, Charanraj; Crepaldi, Marco; Torazza, Diego; Adamatzky, Andrew; Abdi, Gisya; Szkudlarek, Aleksandra; Chiolerio, Alessandro

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.

• Journal Article Type: Article
• Acceptance Date: Jan 1, 2025
• Online Publication Date: Apr 8, 2025
• Publication Date: Jun 21, 2025
• Deposit Date: May 9, 2025
• Publicly Available Date: Jul 25, 2025
• Journal: Materials horizons
• Print ISSN: 0080-4649
• Publisher: Royal Society, The
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Pages: 4193-4207
• DOI: https://doi.org/10.1039/d4mh01592d
• Public URL: https://uwe-repository.worktribe.com/output/14402632