2024-08-21
Introduction:
Pressure sensors are widely used in various applications, but after a period of usage, they often exhibit drift. What exactly causes pressure sensor drift? And how can we minimize this drift during the design process?
Causes of Sensor Drift:
Sensor drift refers to the phenomenon where the output of a sensor changes over time. This drift can lead to inaccurate measurements, impacting the reliability and stability of the sensor in real-world applications. There are several factors that contribute to sensor drift, which are explained below:
1.Temperature Variations:
Temperature changes are one of the most common causes of sensor drift. As temperatures fluctuate, the materials within the sensor expand and contract, affecting both the sensor’s mechanical structure and its electrical properties, leading to output drift. For instance, an increase in temperature may cause the resistance in a resistive sensor to rise, resulting in a higher-than-expected output.
2.Power Supply Fluctuations:
The output of a sensor is influenced by its power supply. When the supply voltage changes, the output can also fluctuate. This occurs because variations in power can alter the operational state of the sensor’s internal circuits, impacting the amplitude and stability of the output signal.
3.Prolonged Use:
Long-term usage can also contribute to sensor drift. Over time, the sensor may be affected by mechanical stress, chemical exposure, or thermal cycling, causing its internal structure to degrade and leading to output deviations. External factors such as vibration or impact can further accelerate this drift.
4.Sensor Aging:
As a sensor ages, its performance may degrade, resulting in drift. The materials and components inside the sensor gradually wear out, causing changes in its physical properties. For example, the electrolytes inside the sensor may deplete over time, reducing its sensitivity and stability, which in turn leads to output drift.
5.Environmental Influences:
Environmental factors such as atmospheric pressure, humidity, and light exposure can also cause sensor drift. These changes can affect the interaction between the sensor and the object being measured, altering the accuracy and stability of the sensor’s readings.
Technical Analysis of Temperature-Induced Zero-Point Drift:
During the early stages of pressure sensor development, diffusion silicon chips were bonded to metal bases using glass powder. A significant drawback of this method was that it created considerable stress around the pressure-sensitive chip. Even after annealing, residual stress could not be fully eliminated. When temperature changes occur, the differing thermal expansion coefficients of metal, glass, and the silicon chip generate thermal stress, causing zero-point drift in the sensor. This is why zero-point thermal drift in sensors is much larger than the chip’s own zero-point thermal drift.
Another factor is soldering using silver paste and terminal posts, which, if not handled correctly, can lead to unstable contact resistance, especially during temperature changes. These issues contribute to significant zero-point and thermal drift in sensors.
From a semiconductor perspective, zero-point thermal drift occurs because the doping concentration and resistance value of resistors must be uniform to minimize output voltage drift in the Wheatstone bridge. Achieving this uniformity during diffusion is challenging, so pressure-sensitive resistors should be as close and as short as possible to reduce drift.
From a circuit design perspective, ideal Wheatstone bridge circuits would have equal resistor values. However, zero-point thermal drift is caused by the temperature-dependent change in diffusion resistor values. Over a specific temperature range, resistor values increase with temperature, resulting in positive temperature coefficients for diffusion resistors.
Solutions to Drift:
Pressure sensor zero-point drift compensation can be approached from two directions: hardware compensation and software compensation.
Hardware Compensation Methods:
Adding constant resistors in series or parallel to the bridge arms
Using thermistors in the bridge arms for temperature compensation
External thermistor compensation
Employing double-bridge and transistor compensation techniques
Optimizing circuit design can also reduce the impact of sensor drift. For example, employing temperature compensation circuits can correct for temperature-induced output changes, improving measurement accuracy and stability. Additionally, filtering, amplification, and other circuit design techniques can be used to minimize the effects of power supply fluctuations and environmental interference on sensor performance.
Software Compensation Methods:
During signal acquisition, input signals may be zero during periods before triggering and after acquisition ends. However, output signals may not be zero due to random noise, making the collected data irrelevant for calculations. We define this signal as zero-point drift. Software solutions include:
Polynomial Fitting: In practical measurements, the relationship between pressure and temperature readings and sensor output is often nonlinear, typically modeled by polynomials. Polynomial fitting involves solving for coefficients to best represent the signal.
RBF Neural Networks: This method offers an alternative to more complex formula-based compensation algorithms. It requires fewer samples and is simpler to implement, offering strong potential for accurate compensation due to its ability to approximate arbitrary functions.
Other methods include lookup tables and interpolation.
Practical Measures to Reduce Drift:
1.Stabilize Temperature: Keep sensors in a constant temperature environment to avoid fluctuations.
2.Implement Temperature Compensation: Incorporate temperature sensors within the sensor unit to adjust for changes.
3.Select Proper Adhesion Methods: Choose bonding methods that reduce mechanical stress.
4.Use Independent Amplifiers: Employ separate amplifiers to minimize external influences on signal integrity.
5.Adopt Auto-Calibration Techniques: Auto-calibration helps maintain consistent output under varying environmental conditions.
6.Choose High-Precision Sensors: High-precision sensors have lower drift, improving measurement reliability.
7.Process Drift Data: Analyze data over time and apply averaging techniques to minimize drift impact.
