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Updated: Feb 6, 2026
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A Method for Growing Bio-memristors from Slime Mold
Published on: November 2, 2017
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Reconfigurable In-Sensor Computing Memristor for Olfactory SNN and Reservoir Hybrid Neuromorphic Computing
Lin Lu1, Jinhao Zhang1, Qingxin Chen1
1Shandong Key Laboratory of Next-Generation Semiconductor Technology and Systems, School of Integrated Circuits, Shandong University, Jinan 250100, China;Suzhou Research Institute, Shandong University, Suzhou 215123, China.
Research (Washington, D.C.)
|February 5, 2026
Summary
This study introduces a novel memristor for in-sensor computing, enabling low-power, low-latency gas sensing. The device integrates neuromorphic functions for efficient hazardous gas monitoring and control.
Area of Science:
- Materials Science
- Neuroscience
- Electrical Engineering
Background:
- Traditional gas sensors face efficiency issues due to von Neumann architectures.
- There is a need for low-power, low-latency olfactory systems with in-sensor computing capabilities.
Purpose of the Study:
- To propose a reconfigurable neuromorphic heterostructure memristor (MXene@SnS2@PANI) for in-sensor computing.
- To develop an olfactory system with integrated computational functions for gas sensing.
Main Methods:
- Fabrication of a MXene@SnS2@PANI heterostructure memristor.
- Demonstration of reconfigurable responses to electrical and gas stimuli.
- Implementation of integrate-and-fire neuronal dynamics and neuromorphic synaptic behaviors.
Main Results:
- The memristor exhibits both synaptic and neuronal computational functions in a single device.
- Reconfigurable neuromorphic computing features with volatile and nonvolatile conductance updates were achieved.
- Successful real-time hazardous gas monitoring and automated ventilation control were demonstrated.
Conclusions:
- The proposed device offers a novel approach to neuromorphic olfactory systems, minimizing circuit complexity.
- This technology paves the way for next-generation intelligent sensing systems.
- The system enables efficient gas recognition and concentration identification through neuromorphic computing.
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