A smart high accuracy silicon piezoresistive pressure sensor temperature compensation system

Area of Science:

• Sensor Technology
• Materials Science
• Artificial Intelligence

Background:

• Silicon piezoresistive pressure sensors suffer from accuracy degradation due to thermal drift.
• Nonlinear temperature variations significantly impact sensor performance.

Purpose of the Study:

• To develop and validate a smart temperature compensation system for silicon piezoresistive pressure sensors.
• To enhance sensor accuracy and reliability across a wide temperature range (-40 to 85 °C).

Main Methods:

• Designed a conditioning circuit for signal processing and data acquisition.
• Developed a LabVIEW program utilizing an extreme learning machine (ELM) for pressure drift calibration.
• Ported the ELM algorithm to a micro-control unit (MCU) for real-time compensation.

Main Results:

• Achieved significant improvements in sensor accuracy, linearity, zero temperature coefficient, and sensitivity temperature coefficient.
• Accuracy improved from 2.57% FS to 0.13% FS; linearity from 2.49% FS to 0.15% FS.
• Demonstrated the system's effectiveness in compensating for thermal drift between -40 and 85 °C.

Conclusions:

• The proposed smart temperature compensation system effectively enhances the accuracy of silicon piezoresistive pressure sensors.
• The ELM algorithm offers superior performance and suitability for batch compensation compared to other methods.
• The compensated sensors are validated for high-accuracy pressure measurement in oil-gas pipelines.