Low power tactile sensory neuron using nanoparticle-based strain sensor and memristor

Abstract

Endowing strain sensors with neuromorphic computing capabilities could permit the efficient processing of tactile information on the edge. The realization of such functionalities from a simple circuit without software processing holds promise for attaining skin-based perception. Here, leveraging the intrinsic neuronal plasticity of memristive neurons, various firing patterns induced by the applied strain were demonstrated. More specifically, tonic, bursting, transition from tonic to bursting, adaptive, and nociceptive activities were captured. The implementation of these patterns permits the facile translation of the analog pressure signals into digital spikes, attaining accurate perception of various tactile characteristics. The tactile sensory neuron consisting of an RC circuit was composed of a SiO2-based conductive bridge memristor exhibiting leaky integrate-and-fire properties and a Pt nanoparticles (NPs)-based strain sensor with a gauge factor of ∼270. A dense layer of Pt NPs was also used as the bottom electrode for the memristive element, yielding the manifestation of a threshold switching mode with a switching voltage of only ∼350 mV and an exceptional switching ratio of 107. Our work provides valuable insights for developing low power neurons with tactile feedback for prosthetics and robotics applications.