Structures play a very important role in developing pressure sensors with good sensitivity and linearity, as they undergo deformation to the input pressure and function as…
Structures play a very important role in developing pressure sensors with good sensitivity and linearity, as they undergo deformation to the input pressure and function as the primary sensing element of the sensor. To achieve high sensitivity, thinner diaphragms are required; however, excessively thin diaphragms may induce large deflection and instability, leading to the unfavorable performances of a sensor in terms of linearity and repeatability. Thereby, importance is given to the development of innovative structures that offer good linearity and sensitivity. This paper aims to investigate the sensitivity of a bossed diaphragm coupled fixed guided beam three-dimensional (3D) structure for pressure sensor applications.
The proposed sensor comprises of mainly two sensing elements: the first being the 3D mechanical structure made of bulk silicon consisting of boss square diaphragm along with a fixed guided beam landing on to its center, forming the primary sensing element, and the diffused piezoresistors, which form the secondary sensing element, are embedded in the tensile and compression regions of the fixed guided beam. This micro mechanical 3 D structure is packaged for applying input pressure to the bottom of boss diaphragm. The sensor without pressure load has no deflection of the diaphragm; hence, no strain is observed on the fixed guided beam and also there is no change in the output voltage. When an input pressure P is applied through the pressure port, there is a deformation in the diaphragm causing a deflection, which displaces the mass and the fixed guided beam vertically, causing strain on the fixed guided beam, with tensile strain toward the guided end and compressive strain toward the fixed end of the close magnitudes. The geometrical dimensions of the structure, such as the diaphragm, boss and fixed guided beam, are optimized for linearity and maximum strain for an applied input pressure range of 0 to 10 bar. The structure is also analyzed analytically, numerically and experimentally, and the results are compared.
The structure offers equal magnitudes of tensile and compressive stresses on the surface of the fixed guided beam. It also offers good linearity and sensitivity. The analytical, simulation and experimental studies of this sensor are introduced and the results correlate with each other. Customized process steps are followed wherein two silicon-on-insulator (SOI) wafers are fusion bonded together, with SOI-1 wafer used to realize the diaphragm along with the boss and SOI-2 wafer to realize the fixed guided beam, leading to formation of a 3D structure. The geometrical dimensions of the structure, such as the diaphragm, boss and fixed guided beam, are optimized for linearity and maximum strain for an applied input pressure range of 0 to10 bar.
This paper presents a unique and compact 3D micro-mechanical structure pressure sensor with a rigid center square diaphragm (boss diaphragm) and a fixed guided beam landing at its center, with diffused piezoresistors embedded in the tensile and compression regions of the fixed guided beam. A total of six masks were involved to realize and fabricate the 3D structure and the sensor, which is presumed to be the first of its kind in the fabrication of MEMS-based piezoresistive pressure sensor.
Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very…
Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.
A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.
Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.
The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.