Calibrating MEMS Pressure Sensors Using Correlation Coefficients
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Solution Overview
Problem
Calibrating micro-electromechanical systems like pressure sensors is time-consuming and costly due to the need for individual calibration of each device, especially given their nonlinearity.
Innovation Solution
A method involving a training phase where correlation coefficient values are determined through physical and electrical tests on a first pressure sensor, allowing for the efficient calibration of multiple pressure sensors during a testing phase without requiring each sensor to undergo a full pressure or voltage sweep.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each pressure sensor is individually calibrated through physical testing, then calibration accuracy is improved, but calibration time and cost increase significantly
Solution Approach 1:
The patent performs physical calibration tests on a first pressure sensor during a training phase to establish correlation coefficient values before the testing phase. These pre-determined correlation coefficients are then used to calibrate second pressure sensors without requiring full physical testing, thereby reducing calibration time while maintaining accuracy through the preliminary characterization of sensor behavior
Solution Approach 2:
The patent creates a mathematical model (copy) of the pressure sensor's capacitance-pressure relationship through correlation coefficient values derived from training data. This model is then applied to predict and determine calibration parameters for additional sensors, replacing the need for exhaustive physical testing on each individual sensor while preserving calibration accuracy
2Measurement precision
If each pressure sensor is individually calibrated through physical testing, then calibration accuracy is improved, but equipment cost increases
Solution Approach 1:
The patent develops a universal calibration approach where correlation coefficient values obtained from training a first pressure sensor can be applied to calibrate multiple second pressure sensors. This multi-functional methodology allows a single set of training experiments to serve the calibration needs of many sensors, reducing the requirement for extensive specialized calibration equipment and infrastructure
Solution Approach 2:
By creating a mathematical representation (copy) of the sensor's response characteristics through correlation coefficients, the patent eliminates the need for complex physical calibration setups for each subsequent sensor. The mathematical model serves as a substitute for expensive physical calibration equipment, achieving the same calibration accuracy at lower equipment cost
3Productivity
If correlation coefficient values are used to calibrate multiple sensors, then calibration efficiency is improved, but individual sensor differences may reduce precision
Solution Approach 1:
The patent applies local quality by determining correlation coefficient values specifically for each pressure sensor based on its individual characteristics. During the training phase, the first pressure sensor's unique capacitance-voltage-pressure relationships are captured, and these sensor-specific correlation coefficients are then used to calibrate second pressure sensors, accounting for individual variations while maintaining efficiency
Solution Approach 2:
The patent utilizes parameter changes by varying voltage and pressure conditions during the training phase to capture the full range of the pressure sensor's behavior. By establishing correlation coefficients across multiple operating points (different voltages and pressures), the model adapts to the sensor's non-linear characteristics and can accurately predict capacitance values across the entire operating range, maintaining precision while improving efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the calibration of a large quantity of pressure sensors using correlation coefficient values from a lesser number of sensors, reducing the time and cost associated with calibration by predicting capacitance values for second pressure sensors without physical testing.
Implementation Method 1
determining first capacitance values in response to applying first voltage values to the first pressure sensor
Implementation Method 2
determining second capacitance values in response to applying second voltage values to the second pressure sensor
Data Source
AI summary
Methods and apparatus to calibrate micro-electromechanical systems are disclosed. An pressure sensor calibration apparatus includes a pressure chamber in which a first pressure sensor is to be disposed; one or more first sensors to measure a first capacitance value from the first pressure sensor from a physical test performed; the one or more first sensors to measure a second capacitance value from a first electrical test performed on the first pressure sensor; and a correlator to determine correlation coefficient values based on the first capacitance value determined during the physical test on the first pressure sensor and the second capacitance value determined during the first electrical test on the first pressure sensor; and a calibrator to determine calibration coefficient values to calibrate a second pressure sensor based on the correlation coefficient values and a third capacitance value determined during a second electrical test on the second pressure sensor.