A Smart High Accuracy Silicon Piezoresistive Pressure Sensor Temperature Compensation System

The multi-parameter measurement of submersible electric pump is an important part of monitoring data underground. The system measures the temperature, pressure, leakage current and vibration signals, which return through the power line carrier technology and data processing on the oil well. This article also focuses on the pressure sensor temperature compensation algorithm, using an anti-linear curve fitting to approximating, and effectively eliminates the error of silicon pressure sensor resulting from temperature changing. The system has brought about a striking effect in experiments of oil field.

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A Method for Vibration-Cylinder Air-Pressure Sensor Temperature Compensation Based on Radial Basis Function (RBF) Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.236-237.1232 ◽

2012 ◽

Vol 236-237 ◽

pp. 1232-1237

Author(s):

Yan Ren ◽

Duan Xu ◽

Fang Ling Qin

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Pressure Sensor ◽

Basis Function ◽

Temperature Compensation ◽

Rbf Neural Network ◽

Air Pressure ◽

Environment Temperature ◽

Radial Basis ◽

Sensor Temperature

There is a nonlinear measurement error of the vibration-cylinder air-pressure sensor when the environment temperature changes; To solve the problem, the paper carried out a research on vibration-cylinder air-pressure sensor temperature compensation based on radial basis function(RBF) neural network; The temperature error characteristics of the sensor was studied; The sensor temperature compensation of RBF neural network structures and algorithms was designed; The centers, variances, weights, and the hidden layer neuron number of the radial basis function were determined. The experiments showed that the trained RBF neural network can approximate the input-output relationship of the vibration-cylinder air-pressure sensor in high accuracy.

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A temperature compensation system for silicon pressure sensor based on neural networks

The 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems (NEMS) ◽

10.1109/nems.2014.6908851 ◽

2014 ◽

Cited By ~ 2

Author(s):

Guanwu Zhou ◽

Yulong Zhao ◽

Fangfang Guo

Keyword(s):

Neural Networks ◽

Pressure Sensor ◽

Temperature Compensation ◽

Compensation System

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A Highly Accurate, Polynomial-Based Digital Temperature Compensation for Piezoresistive Pressure Sensor in 180 nm CMOS Technology

Sensors ◽

10.3390/s20185256 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5256

Author(s):

Imran Ali ◽

Muhammad Asif ◽

Khuram Shehzad ◽

Muhammad Riaz Ur Rehman ◽

Dong Gyu Kim ◽

...

Keyword(s):

Pressure Sensor ◽

Temperature Compensation ◽

Absolute Temperature ◽

Cmos Process ◽

Measured Temperature ◽

Signal Conditioning ◽

Clock Frequency ◽

Linear Temperature ◽

Piezoresistive Pressure Sensor ◽

Wide Range

Recently, piezoresistive-type (PRT) pressure sensors have been gaining attention in variety of applications due to their simplicity, low cost, miniature size and ruggedness. The electrical behavior of a pressure sensor is highly dependent on the temperature gradient which seriously degrades its reliability and reduces measurement accuracy. In this paper, polynomial-based adaptive digital temperature compensation is presented for automotive piezoresistive pressure sensor applications. The non-linear temperature dependency of a pressure sensor is accurately compensated for by incorporating opposite characteristics of the pressure sensor as a function of temperature. The compensation polynomial is fully implemented in a digital system and a scaling technique is introduced to enhance its accuracy. The resource sharing technique is adopted for minimizing controller area and power consumption. The negative temperature coefficient (NTC) instead of proportional to absolute temperature (PTAT) or complementary to absolute temperature (CTAT) is used as the temperature-sensing element since it offers the best temperature characteristics for grade 0 ambient temperature operating range according to the automotive electronics council (AEC) test qualification ACE-Q100. The shared structure approach uses an existing analog signal conditioning path, composed of a programmable gain amplifier (PGA) and an analog-to-digital converter (ADC). For improving the accuracy over wide range of temperature, a high-resolution sigma-delta ADC is integrated. The measured temperature compensation accuracy is within ±0.068% with full scale when temperature varies from −40 °C to 150 °C according to ACE-Q100. It takes 37 µs to compute the temperature compensation with a clock frequency of 10 MHz. The proposed technique is integrated in an automotive pressure sensor signal conditioning chip using a 180 nm complementary metal–oxide–semiconductor (CMOS) process.

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