Boosting Artificial Olfaction: Visual Cues‐Enhanced Gas Classification by a Bimodal Neuromorphic Device

Abstract

ABSTRACT Artificial olfactory sensors have garnered significant attention in various applications, including micro‐robotics, implantable medical devices, and consumer electronics. However, they still face challenges in trade‐offs among high recognition accuracy, compact size, and low power consumption. Existing strategies can rely on large‐scale sensor arrays (up to 10 4 elements) to enhance gas recognition accuracy, but this substantially increases system size and power consumption. Inspired by biological multisensory synergy, we propose a visual–olfactory bimodal neuromorphic device to overcome these limitations. It emulates biological perceptual fusion, including bimodal perceptual weighting and enhancement. With a small active area of 148 µm 2 , a static power consumption of only 3.4 µW, and a low operating voltage of 1 V, the device exhibits ppb‐level sensing performance and is capable of both classifying gas types and identifying concentrations for multiple target gases. The proposed bimodal perception strategy achieves a gas classification accuracy of 98.27%, far exceeding that of the olfactory unimodal mode (52.24%), and, importantly, enables precise discrimination of mixed gases with highly overlapping sensing signatures. Our strategy not only provides a unit architecture for constructing miniaturized, low‐power, and highly accurate artificial olfactory systems but also paves the way for next‐generation bio‐inspired multimodal neuromorphic sensing.