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Machine Learning and Swarm Optimization Algorithm in Temperature Compensation of Pressure Sensors
Hexing Wang1,2, Jia Li1,2
1Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China.
Sensors (Basel, Switzerland)
|November 11, 2022
Summary
This study introduces novel temperature compensation methods for MEMS piezoresistive pressure sensors using swarm optimization and machine learning. Optimized algorithms significantly enhance sensor accuracy across different operating ranges.
Area of Science:
- Sensor Technology
- Machine Learning
- Optimization Algorithms
Background:
- MEMS piezoresistive pressure sensors require accurate temperature compensation for reliable performance.
- Existing software compensation methods often lack sensor-specific range optimization and disregard operating characteristics.
- Developing tailored compensation algorithms is crucial for improving sensor output accuracy.
Purpose of the Study:
- To propose and evaluate novel temperature compensation methods for MEMS piezoresistive pressure sensors.
- To investigate the effectiveness of swarm optimization algorithms fused with machine learning for different sensor ranges.
- To determine the optimal calibration dataset partitioning ratio for enhanced compensation performance.
Main Methods:
- Development of three temperature compensation algorithms integrating swarm optimization and machine learning.
- Application of algorithms to MEMS piezoresistive pressure sensors across distinct operational ranges.
- Experimental analysis of calibration dataset partitioning ratios on Sensor A performance.
- Comparative evaluation against existing state-of-the-art compensation algorithms.
Main Results:
- Different swarm optimization and machine learning algorithms demonstrated suitability for specific pressure sensor ranges.
- Optimal temperature compensation on Sensor A was achieved with a 33.3% data split ratio.
- Achieved zero-drift coefficient of 2.88 × 10-7/°C and sensitivity temperature coefficient of 4.52 × 10-6/°C.
- Proposed algorithms outperformed existing methods in literature.
Conclusions:
- Swarm optimization and machine learning fusion offers a powerful approach for MEMS pressure sensor temperature compensation.
- Sensor-specific range optimization and calibration data partitioning are critical for maximizing compensation effectiveness.
- The findings provide a significant advancement in achieving high-accuracy MEMS pressure sensing.
Keywords:
dataset partitioningdifferential evolution algorithmpressure sensorquantum particle swarm optimizationtemperature compensationMore Related Videos
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