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Multisensory Neuromorphic Devices: From Physics to Integration
An Gui1,2, Haoran Mu3,4, Rong Yang5
1College of Semiconductors (College of Integrated Circuits), Hunan University, Changsha, 410000, People's Republic of China.
Nano-Micro Letters
|January 11, 2026
Summary
Neuromorphic systems are advancing multisensory perception for the Internet of Things (IoT). This review explores devices mimicking biological systems for efficient, real-time processing of diverse sensory inputs.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Computer Science
Background:
- The Internet of Things (IoT) drives demand for intelligent sensing.
- Traditional systems struggle with complex, multisensory data.
- Neuromorphic devices offer event-driven, low-power solutions.
Purpose of the Study:
- To review physical mechanisms, device behaviors, and integration strategies for multisensory neuromorphic hardware.
- To highlight synaptic mechanisms for cross-modal interaction.
- To analyze device-level signal fusion approaches.
Main Methods:
- Review of recent literature on synaptic devices and material systems.
- Analysis of signal conversion, encoding, and fusion techniques.
- Exploration of biologically inspired processing architectures.
Main Results:
- Synaptic devices show potential for responding to visual, tactile, thermal, and chemical stimuli.
- Challenges exist in signal conversion, encoding compatibility, and data fusion.
- Device-level fusion approaches are being developed.
Conclusions:
- Neuromorphic systems are key to future intelligent sensing.
- Further research needed for efficient, scalable, and biologically inspired multisensory integration.
- Overcoming current challenges will enable advanced real-time perception.
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