Bioinspired Artificial Sensory Nerve Based on Nafion Memristor

TLDR

Bioinspired artificial haptic neuron systems are gaining attention in AI for applications such as healthcare monitoring, electronic skins, and human–machine interfaces. The paper designs an artificial haptic neuron system by integrating a piezoresistive sensor with a Nafion‑based memristor. The system converts mechanical stimuli into electrical signals via a piezoresistive sensor, processes them with a Nafion‑based memristor synapse, and integrates these components to detect tactile stimuli encoded with temporal information such as count, frequency, duration, and speed. The pyramid‑structured sensor delivers high sensitivity (6.7×10⁷ kPa⁻¹ in 1–5 kPa and 3.8×10⁵ kPa⁻¹ in 5–50 kPa) and durability (>7000 cycles), the memristor provides low‑power synaptic functions (10–200 pJ) and remains stable over 10⁴ tests, enabling accurate English character recognition and demonstrating the system’s high performance, durability, and simple fabrication.

Abstract

Abstract Bioinspired artificial haptic neuron system has received much attention in the booming artificial intelligence industry for its broad range of high‐impact applications such as personal healthcare monitoring, electronic skins, and human–machine interfaces. An artificial haptic neuron system is designed by integrating a piezoresistive sensor and a Nafion‐based memristor for the first time in this paper. The piezoresistive sensor serves as a sensory receptor to transform mechanical stimuli into electric signals, and the Nafion‐based memristor serves as the synapse to further process the information. The pyramid‐structured sensor exhibits excellent sensitivity (6.7 × 10 7 kPa −1 in 1–5 kPa and 3.8 × 10 5 kPa −1 in 5–50 kPa) and durability (>7000 cycles), while the memristor realizes fundamental synaptic functions under low power consumption (10–200 pJ) and remains stable for over 10 4 consecutive tests. The integrated system can detect tactile stimuli encoded with temporal information, such as the count, frequency, duration and speed of the external force. As a proof‐of‐concept, English characters recognition with high accuracy can be achieved on the system under a supervised learning method. This work shows promising potential in bioinspired sensing systems owing to the high performance, excellent durability, and simple fabrication procedure.