Abstract
In order to meet the requirement of high sensitivity and signal-to-noise ratios (SNR), this study develops and optimizes a piezoresistive pressure sensor by using double silicon nanowire (SiNW) as the piezoresistive sensing element. First of all, ANSYS finite element method and voltage noise models are adopted to optimize the sensor size and the sensor output (such as sensitivity, voltage noise and SNR). As a result, the sensor of the released double SiNW has 1.2 times more sensitivity than that of single SiNW sensor, which is consistent with the experimental result. Our result also displays that both the sensitivity and SNR are closely related to the geometry parameters of SiNW and its doping concentration. To achieve high performance, a p-type implantation of 5 × 1018 cm−3 and geometry of 10 µm long SiNW piezoresistor of 1400 nm × 100 nm cross area and 6 µm thick diaphragm of 200 µm × 200 µm are required. Then, the proposed SiNW pressure sensor is fabricated by using the standard complementary metal-oxide-semiconductor (CMOS) lithography process as well as wet-etch release process. This SiNW pressure sensor produces a change in the voltage output when the external pressure is applied. The involved experimental results show that the pressure sensor has a high sensitivity of 495 mV/V·MPa in the range of 0–100 kPa. Nevertheless, the performance of the pressure sensor is influenced by the temperature drift. Finally, for the sake of obtaining accurate and complete information over wide temperature and pressure ranges, the data fusion technique is proposed based on the back-propagation (BP) neural network, which is improved by the particle swarm optimization (PSO) algorithm. The particle swarm optimization–back-propagation (PSO–BP) model is implemented in hardware using a 32-bit STMicroelectronics (STM32) microcontroller. The results of calibration and test experiments clearly prove that the PSO–BP neural network can be effectively applied to minimize sensor errors derived from temperature drift.
Highlights
To date, the microelectromechanical system (MEMS) silicon piezoresistive pressure sensors have been used in a diverse range of commercial and engineering applications including consumer, automobiles, biomedicine, process control, military, meteorology, and aerospace industry areas [1,2,3,4,5,6]
With the rapid development of micro/nano processing technology, recently, there have been studies trying to reduce the sizes of conventional MEMS piezoresistive pressure sensors and enhance their low sensitivity by using a wide variety of nanomaterials and nanostructures as sensing elements [7,11,12,13,14,15,16], such as silicon nanorods and silicon nanowires (SiNWs), which have the small size and ultra-high piezoresistive effect [17,18,19,20,21,22,23,24]
The sensor design parameters must be properly chosen to balance the pressure sensitivity and voltage noise sources of the nanoelectromechanical system (NEMS) piezoresistive pressure sensor given a set of design and operating constraints, especially where a high signal-to-noise ratio (SNR) is required for the faithful measurement of the small pressure differentials [26]
Summary
The microelectromechanical system (MEMS) silicon piezoresistive pressure sensors have been used in a diverse range of commercial and engineering applications including consumer, automobiles, biomedicine, process control, military, meteorology, and aerospace industry areas [1,2,3,4,5,6]. The piezoresistive pressure sensors have disadvantage over their large size, low sensitivity and poor signal-to-noise ratios in contrast with high performance of the micromachined capacitive and resonant pressure sensors [5,6,7]. An exhaustive analysis considering the influences of doping concentration and the geometry of SiNW piezoresistors on optimizing the performance of the NEMS piezoresistive pressure sensors in terms of sensitivity and signal-to-noise ratio has been rarely reported till now. In order to obtain high-performance piezoresistive pressure sensors based on silicon on insulator (SOI), we investigate the design, optimization modeling, fabrication, measurement and temperature drift compensation of the NEMS pressure sensor taking into account the balance between the low voltage noise and the high pressure sensitivity. For the sake of eliminating the effects of temperature and improve measurement accuracy, the back-propagation (BP) neural network improved by the particle swarm optimization (PSO) algorithm [28,29] is applied to achieve the temperature drift compensation in the study
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