Issue

Vol. 18 (2026)

Multisensory Neuromorphic Devices: From Physics to Integration

https://doi.org/10.1007/s40820-025-01940-9
An Gui
College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410000, People’s Republic of China
Haoran Mu
Songshan Lake Materials Laboratory, Dongguan 523808, People’s Republic of China
Rong Yang
College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha 410000, People’s Republic of China
Guangyu Zhang
Songshan Lake Materials Laboratory, Dongguan 523808, People’s Republic of China
Shenghuang Lin
Songshan Lake Materials Laboratory, Dongguan 523808, People’s Republic of China

Corresponding Author: Shenghuang Lin

linshenghuang@sslab.org.cn

Nano-Micro Letters, Vol. 18 (2026), Article Number: 113

Abstract

The increasing complexity of intelligent sensing environments, driven by the growth of Internet of Things technologies, has created a strong demand for neuromorphic systems capable of real-time, low-power multisensory perception. Traditional sensory architectures, constrained by single-modal processing and centralized computing, struggle to meet the requirements of diverse and dynamic input conditions. Multisensory neuromorphic devices offer a promising solution by mimicking the distributed, event-driven processing of biological systems. Recent efforts have explored synaptic devices and material systems that respond to various input modalities, including visual, tactile, thermal, and chemical stimuli. However, challenges remain in signal conversion, encoding compatibility, and the fusion of heterogeneous inputs without loss of unisensory information. This review provides a comprehensive overview of the physical mechanisms, device behaviors, and integration strategies that underpin signal processing in neuromorphic hardware. We highlight synaptic mechanisms conducive to cross-modal interaction, analyze representative signal fusion approaches at the device level, and discuss future directions for constructing efficient, scalable, and biologically inspired multisensory neuromorphic systems.


Highlights:
1 This review provides a comprehensive overview of the physical mechanisms, device behaviors, and integration strategies that underpin multimodal signal processing in neuromorphic hardware.
2 This review examines implementation strategies for multimodal integration, including signal fusion methods and processing techniques for handling cross-modal stimuli.
3 This review categorizes multimodal neuromorphic devices into three distinct frameworks and comprehensively discusses their respective advantages and limitations.

Keywords

Neuromorphic computing Multisensory signals Physical mechanism Multisensory fusion Synapse
Gui, An, Haoran Mu, Rong Yang, Guangyu Zhang, and Shenghuang Lin. 2026. “Multisensory Neuromorphic Devices: From Physics to Integration”. Nano-Micro Letters 18 (January):113. https://doi.org/10.1007/s40820-025-01940-9.
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