Bioinspired Nanofluidic Memristors Based on Polyelectrolyte Conformation for Synaptic Learning and in‐Memory Logic Computing

Abstract

ABSTRACT Bioinspired neuromorphic and in‐memory computing requires devices that store and process information through ionic dynamics analogous to biological synapses. Here, we report a polyelectrolyte conformational nanofluidic memristor (PCM) that integrates synaptic plasticity, neuromorphic learning, and stateful ionic logic within a single aqueous platform. The device operates through electric‐field‐driven, reversible conformational transitions of polyelectrolytes confined inside graphene oxide nanochannels, enabling analog conductance tuning, a sharp, tunable switching threshold, and an ON/OFF ratio exceeding 160. These conformational dynamics endow the PCM with rich synaptic functions, including long‐term potentiation and depression, multilevel memory retention, and symmetric weight update rules. By directly mapping the experimentally measured potentiation/depression curves into a physical learning model, we demonstrate high‐accuracy neuromorphic learning, achieving 97.1% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten‐digit dataset. Beyond learning, interconnected PCM units perform stateful ionic OR, IMP, and NAND operations, establishing universal in‐memory logic within the same ionic platform. This work introduces a bioinspired nanofluidic computing paradigm that unites the adaptive learning of neural networks and deterministic logic, paving the way to scalable and brain‐like ionic processors.