Research On Diffustion Silicon Pressure Sensor System

Diffused silicon pressure sensors occupy a vital position in the field of engineering measurement.Intelligent sensors that combine traditional sensor technology with computer technology are the development direction of sensors in recent years.The new smart sensors have the advantages of miniaturization,intelligence,and diversification of functions.The sensor fusion technology has become a new technology hotspot,and the high-precision intelligent sensor measurement system is the focus of our research.In this paper,the working principle and internal structure of the diffused silicon pressure sensor are introduced in detail.The two-dimensional calibration experiment is carried out for temperature and output voltage.The input and output characteristic curves of the diffused silicon pressure sensor are obtained.By analyzing the curve,it is found that The measurement system has three problems that affect its accuracy: first,zero drift;second,output nonlinearity;third,temperature drift.And the solution to these three problems is proposed.The main research contents are as follows.First of all,the zero temperature drift problem,the output voltage of the zero point of the sensor is different at different temperatures.For this problem,the parallel resistance of any bridge arm of the Wheatstone bridge is much larger than the resistance of the bridge arm,so that the measurement system is at any temperature.The zero output is consistent.Secondly,the problem of low linearity of output voltage and temperature will also affect the measurement accuracy of the system.For this problem,the curve fitting method is used to correct the nonlinearity,so that the predicted value obtained by fitting the straight line and the calibration value are The error is minimal.Thirdly,the output voltage of the diffused silicon pressure sensor will drift with temperature.For this problem,the particle swarm optimization BP neural network algorithm(PSO-BP)is proposed to compensate the temperature of the diffused silicon pressure sensor.The global optimization ability of the particle swarm optimization algorithm is used to select the optimal solution for the initial weight and threshold of the BP neural network algorithm,which makes up for the shortcomings of the random selection of the initial value of the traditional BP neural network and overcomes the defect that it is easy to partially fall into the extreme value.Through the two-dimensional calibration experiment,the input and output characteristic curves of the diffused silicon pressure sensor at different working temperatures are obtained.After the PSO-BP algorithm compensates it,the predicted pressure value is output.Experiments show that the output zero temperature coefficient and sensitivity temperature coefficient compensated by the PSO-BP algorithm are increased by an order of magnitude,which reduces the influence of temperature on the sensor.Finally,the STM32F407VET6 chip is designed as the core of the diffusion silicon pressure sensor intelligent measurement system,which can realize the measurement,processing and transmission of data,mainly including the upper computer,the lower computer,the wireless communication module,the DC stabilized power supply module,and the LCD display.Module,serial communication module,button module,reset module and alarm module,and overall debugging of the entire measurement system to ensure the feasibility and accuracy of its system.