Bioinspired Crossmodal Tactile Sensory Nerve for High-Accurate Object Recognition

Abstract

Crossmodal perception enabled by the human somatosensory system can be followed to effectively perceive and analyze multiple sensory signals. Here, we construct an artificial crossmodal sensory neuron system by integrating pressure–temperature bimodal sensors with a Hf_0.5Zr_0.5O₂-based complementary memristor, which emulates the tactile perception, neural coding, and synaptic processing functions of humans. With the developed bimodal sensor, the pressure and temperature information can be collected and further converted to electrical signals with excellent sensitivities of 26 407 kPa⁻¹ and −3.34%°C⁻¹, respectively. The complementary memristor can enable information storage and simulate biological synaptic functions, achieving bioinspired neuromorphic processing of sensory signals. Combined with machine learning, this artificial crossmodal sensory neuron system presents an improved accuracy of 96.67% in recognizing temperatures and shapes of distinct objects. This work demonstrates potential applications of the integrated system in advanced neuromorphic hardware for wearable human-machine interfaces and biomimetic robotics.

Keywords

artificial synapse / HZO-based complementary memristor / neuromorphic computing / object recognition / pressure–temperature bimodal sensor

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2026 The Author(s). Energy & Environmental Materials published by John Wiley & Sons Australia, Ltd on behalf of Zhengzhou University.