Pressure sensors based on MEMS (Micro Electro Mechanical Technology) based on micro/nano technology, small size and light weight, can provide higher precision, lower power consumption, better stability and consistency, and work in The superior ability in extreme temperature and humidity environment is the leading technology in the industry. This technology was developed and launched by Fairster China. By integrating Firstrate's unique digital sensor and the Secure-M accessory, it eliminates the trimmer potentiometer on the sensor, ensuring undisturbed data after commissioning, making it ideal for high-tech industries, laboratories, aerospace, vacuum systems, etc. service. The Firstrate pressure sensor has ±0.05% FS end accuracy or 0.1% FS reading accuracy to ensure reliable and reliable test results throughout the test. Impact on the environment temperature is almost negligible, the temperature error within a broad temperature range which only an advanced MEMS (micro-electromechanical) and the MPT (micro-assembly) process;
Micro-Electro-Mechanic System (MEMS) is an advanced manufacturing technology platform. It is based on semiconductor manufacturing technology. MEMS technology uses a series of existing technologies and materials such as lithography, etching, and thin film in semiconductor technology. Therefore, from the manufacturing technology itself, the basic manufacturing technology in MEMS is mature. However, MEMS is more focused on ultra-precision machining, and involves many fields of microelectronics, materials, mechanics, chemistry, and mechanics.
With the development of circuits and industry, the development of multi-chip module (MCM) technology, flip chip (FC) and multi-layer ceramic substrate technology, on the high-density multilayer interconnection circuit board, using assembly and packaging processes, micro-miniature Electronic components are assembled into high-density, high-speed, high-reliability three-dimensional electronic products. This high-density assembly technology is micro-packaging technology.
The thickness of the silicon diaphragm is 3 to 4 μm, which affects a range of the pressure sensor. The accuracy of the thickness of the silicon diaphragm affects the yield and consistency of the sensor. Therefore, it is a huge challenge to operate the connection of the diaphragm to other components at this order of magnitude without affecting the performance of the single crystal silicon. In addition to MEMS and macro-mechanical systems, many physical phenomena are very different. For example, as the size decreases, the effects of inertial force, volume force, and electromagnetic force proportional to the size of the third power will be significantly weakened; and the viscous force, surface force, and static ratio proportional to the size of the second power. The effects of electricity and friction are significantly enhanced and become a major factor affecting micromechanical performance. The calculation methods and theories commonly used in macroscopic machinery will no longer apply.
Outstanding Firstrate engineers combine the developments of micro-kinetics, microfluidics, micro-thermodynamics, micro-tribology, micro-optics and microstructures to achieve monocrystalline wafers and other components with a green, lead-free soldering process A lossless connection between. The volume of MEMS devices has been greatly reduced, and the power consumption has been reduced by more than 80% while the volume of conventional devices has been reduced by more than 90%.
Second, advanced temperature compensation technology
The material characteristics determine that the silicon piezoresistive pressure sensor will cause the sensor output to change when the operating temperature t changes while the input pressure P value is constant. In order to eliminate the influence of non-target parameters (temperature) on the output characteristics of the sensor, various intelligent compensation techniques can be employed.
At present, software compensation methods mainly include interpolation method, curve and surface fitting method, table lookup method and BP neural network method. In interpolation, the data is assumed to be correct, requiring some way to describe what is happening between the data. The curve fitting method is to find a smooth curve, which is the best fit data, but does not have to go through any data points. The look-up table method preloads a series of parameters into a parameter list, and after obtaining the measurement data, it is processed according to the corresponding parameters. The lookup method requires a large amount of storage space and is not suitable for microprocessors. The neural network method establishes an artificial neural network model and determines network parameters through sample training.
Firstrate designed a compensation method based on factor analysis and RBF neural network for temperature drift of silicon piezoresistive pressure sensor. RBF neural network is a special kind of 3-layer neural network in feedforward neural network. It is typical. Local approximation to the neural network has the advantage of fast learning and not falling into local optimum. The RBF neural network is a novel and effective feedforward neural network. It has high computational speed and strong nonlinear mapping ability, and can globally approximate a nonlinear function with arbitrary precision. The method realizes the screening and dimension reduction of the original information through factor analysis, which not only reduces data redundancy, but also eliminates the influence of related and repeated data, and forms a new training sample set. Combining the nonlinear mapping, adaptive ability and strong fault tolerance of RBF neural network to model the compensation process, reducing the input of the network, simplifying the network structure, speeding up convergence, saving running time, and greatly improving the learning rate of the network. Generalization. The RBF neural network based on factor analysis effectively solves the problem of static voltage zero drift and sensitivity drift of the sensor under wide-area temperature changes, and improves the stability of the sensor.
Firstrate's self-developed fully automatic temperature compensation calibration system dynamically updates the calibration result data during the temperature compensation calibration process. The system first opens the internal sensor laboratory verification data file, reads the physical quantity and output millivolts to the memory variable, and sets the multimedia timer parameters. According to the selected channel and sampling rate, the scanning table is downloaded and the acquisition device is started for acquisition. Through the timer interrupt service program and the GPIB bus, the millivolts corresponding to the standard pressure output of the sensor laboratory calibration is sent to the programmable power supply. After the collected data is stable, the acquisition buffer data is read into the computer memory, and the record is saved. . After the channel verification is completed, calculate the channel slope, intercept, standard deviation, correlation coefficient, and display the verification result. Write the check number to the acquisition database and save it to the self-calibration file. When the saved file name exists in the current directory. When the program backs up the original file, it saves the file. Automatic calibration is complete.
Third, excellent induction (elastic) structural design
The pressure sensitivity of the silicon piezoresistive microsensor is related to the thickness and size of the silicon diaphragm and the resistance of the varistor. It is also related to the distribution of the resistor on the silicon diaphragm and the position on the silicon diaphragm. Through finite element analysis, Firstrate's researchers analyzed the relationship between the output voltage sensitivity of the bridge circuit composed of varistor and the position and direction of the resistance, and determined the pressure sensitivity extreme value of the sensor and its condition; The relationship between the piezoresistive coefficient and the transverse piezoresistive coefficient and the direction, and the extremum is obtained, and the optimal position of the linear and output values ​​is found.
To ensure long-term stability of the pressure sensor, how to improve the overload capacity of the sensor is particularly important. Using finite element analysis, Firstrate engineers adjusted the thickness of the sacrificial layer while ensuring linear response within the full-scale range of the sensor. The proper contact between the elastic diaphragm and the substrate effectively improved the overload protection capability of the sensor. The product can achieve 300% FS overpressure self-recovery, 500% FS damage capacity.
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