Study of long-term drift of a porous silicon humidity sensor and its compensation using ANN technique

Islam, Tarikul; Saha, Hiranmay

doi:10.1016/j.sna.2006.03.019

Cited by 28 publications

(22 citation statements)

References 18 publications

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“…This is due to the facts that after oxidation the sensor output was stabilized and the short term drifts due aging was negligible. The long‐term drift if any, can be compensated by adopting the artificial neural network‐based signal processing technique which was used successfully to compensate the drift due to aging of PS‐based RH sensor 18.…”

Section: Resultsmentioning

confidence: 99%

Porous silicon in low moisture content dry gas by impedance spectroscopy

Islam

Hussain

Gangopadhyay

et al. 2011

Physica Status Solidi (a)

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The present work is aimed to develop porous silicon (PS)-based sensor to measure very low humidity concentration with possible industrial applications. This work is in continuation of our previous results reported (Islam et al., Sens. Lett. 6, 746 (2008)) that the PS layer with pore morphology above 10 nm shows good response above 50 ppmV. To obtain improved sensitivity in the lower range below 50 ppmv, the porous structure with pore dimensions below 10 nm is fabricated by controlling the anodization parameters using precision Solatron Electrochemical Cell (1280C). The sensor based on capacitive principle has porous top metal electrode having pores above 1 mm formed on the porous layer by screen printing technique. The mesh structure increases the area of the electrodes and allows the moisture molecules penetrating through the mesh thus improving both sensitivity and ohmic nature of the contacts. The electrical properties were evaluated by impedance spectroscopy for different contents of water vapour in N 2 gas with Agilent 4294A Impedance Analyzer.

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Section: Resultsmentioning

confidence: 99%

Porous silicon in low moisture content dry gas by impedance spectroscopy

Islam

Hussain

Gangopadhyay

et al. 2011

Physica Status Solidi (a)

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“…The aging of the sensor material is a very slow process at room temperature and thus results in continuous changes with time in the structure of pressure sensor (creep) and its physical parameters. It is noted that none of the stabilizing treatments can prevent the aging completely, so data fusion techniques should be utilized to compensate the long-term drift [11,23]. In the paper, the long-term drift compensation for the array of pressure sensors is based on periodic training of the GA-WNN compensation algorithm, which has the capability to learn dynamic systems through a retraining process using new data patterns.…”

Section: Long-term Drift Compensationmentioning

confidence: 99%

Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

Zhang

Liu

et al. 2015

Micromachines

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This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN) which is improved by genetic algorithm (GA) to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in OPEN ACCESSMicromachines 2015, 6 555 hardware by using the 32-bit STMicroelectronics (STM32) microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement.

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“…Calibration issues concerning chemical and biosensors are discussed in [19,20]. Methods for drift compensation in humidity, pressure and gas sensors have been developed based on Neural Networks [21,22], using wavelet transforms [23], Kalman filtering [24], and other learning techniques [25]. These methods are computationally too expensive for low-cost sensors.…”

Section: Overviewmentioning

confidence: 99%

Network calibration of embedded sensors

Papachristou

Bhunia

Wolff

2010

Proceedings of the IEEE 2010 National Aerospace &Amp; Electronics Conference

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The focus of this project is to provide methods for calibration of sensor nodes in sensor networks. The importance of the calibration problem is to compensate for the sensor reading drifts that occur due to systematic errors, noise or sensor degradation. The objective is to provide calibration techniques that apply on collaborative sensors autonomously, i.e. without supervision. Our work involves: a) distributed procedures to identify erroneous sensors; and b) a simulator framework for a sensor net drift calibration setups. Overview• Motivation. The deployment of sensor networks has been growing in scale and scope with new applications in embedded and challenging environments. This progress has been facilitated by advances in nanoscale electronics that can be integrated with MEMS optical and biochemical technologies to build tiny sensor nodes, [1]. However, there are several issues and difficulties in building such sensor nets. In addition to reliability concerns of the sensor nodes and their communications, there is a significant problem with the drift of sensor readings from their correct values. A variety of sensor applications use MEMS for realizing the transduction mechanism. Along with degradations in the micromechanical parts due to environmental effects, wear and tear [2, 3], the signal conditioning and read-out circuitry also suffer from both systematic and random degradations inducing errors in the sensor reads.Sensor calibration has been used over time to correct the reading drifts. Traditionally, calibration is applied on sensors at the micro level, meaning on individual sensor nodes either at the factory or in the network off-line. However, micro-level calibration may not be feasible due to many difficulties such as remote access, security, size of sensor net and device degradations. There is need for network based calibration, i.e. autonomous calibration by collaborating sensor nodes.• Related Work. The problem of calibration of sensor nodes has received considerable attention in research works. Earlier calibration was performed on each sensor at the factory or in the field using simple built-in techniques [4]. Calibration in traditional sensor networks is still being done individually.Most of the recent research has focused on location discovery of sensor nodes. The early work on SpotON [5] modeled the signal to distance relation between transmitting and receiving sensors off-line. Another project targeting location discovery is Calamari, [6], which formulates a calibration approach to sensor localization as a parameter estimation problem. A two-phase collaborative technique is used in [7] where first all pair-wise calibration functions are found and then they are optimized to produce the global calibration function for the net. In the SCAAT project [8], a mathematical technique is used for tracking position and orientation while performing sensor autocalibration. Dynamic fine-grain localization is reported in [9]. More recently. a calibration approach to location estimation is taken in [10] using ...

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Study of long-term drift of a porous silicon humidity sensor and its compensation using ANN technique

Cited by 28 publications

References 18 publications

Porous silicon in low moisture content dry gas by impedance spectroscopy

Porous silicon in low moisture content dry gas by impedance spectroscopy

Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

Network calibration of embedded sensors

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