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An LNN Model for the Dynamic Response of MOS Gas Sensors
Peiwen Wu1, Siyuan Wu1, Guixin Jin2
1State Key Laboratory of Electronic Thin Films and Integrated Devices, School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.
ACS Sensors
|July 27, 2026
Summary
This study introduces a novel liquid neural network model for metal oxide semiconductor (MOS) gas sensors. The model generates extensive training data from limited samples, enhancing gas detection accuracy for electronic noses.
Area of Science:
- Sensor technology
- Artificial intelligence
- Chemical sensing
Background:
- Electronic noses are vital for agriculture, petrochemicals, and environmental monitoring.
- Improving gas classification accuracy and detection precision is crucial.
- Neural networks in electronic noses require large training datasets.
Purpose of the Study:
- To develop a dynamic response model for MOS gas sensors.
- To generate large training datasets from limited test data.
- To enhance gas concentration prediction accuracy.
Main Methods:
- Utilized 14 state variables based on gas adsorption/desorption, chemical reactions, and carrier transport.
- Constructed a sensor dynamic response model using a liquid neural network.
- Validated the model through simulations and experiments.
Main Results:
- The model effectively generated large training datasets from small amounts of test data.
- Achieved higher concentration prediction accuracy compared to existing models.
- Demonstrated the model's capability in enhancing electronic nose performance.
Conclusions:
- The developed liquid neural network model significantly improves MOS gas sensor data generation.
- This approach enhances the precision and accuracy of electronic noses.
- The model offers a viable solution for training data scarcity in gas sensing applications.
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