3D thermo-electro-mechanical simulations of gas sensors based on SOI membranes

3D thermo-electro-mechanical device simulations are presented of a novel fully CMOS-compatible MOSFET gas sensor operating in a SOI membrane. A comprehensive stress analysis of a Si-SiO2-based multilayer membrane has been performed to ensure a high degree of mechanical reliability at a high operating temperature (e.g. up to 400°C). Moreover, optimisation of the layout dimensions of the SOI membrane, in particular the aspect ratio between the membrane length and membrane thickness, has been carried out to find the best trade-off between minimal device power consumption and acceptable mechanical stress.

Multi-field simulations and characterization of CMOS-MEMS high-temperature smart gas sensors based on SOI technology

This paper describes multiple field-coupled simulations and device characterization of fully CMOS-MEMS-compatible smart gas sensors. The sensor structure is designated for gas/vapour detection at high temperatures (>300 °C) with low power consumption, high sensitivity and competent mechanic robustness employing the silicon-on-insulator (SOI) wafer technology, CMOS process and micromachining techniques. The smart gas sensor features micro-heaters using p-type MOSFETs or polysilicon resistors and differentially transducing circuits for in situ temperature measurement. Physical models and 3D electro-thermo-mechanical simulations of the SOI micro-hotplate induced by Joule, self-heating, mechanic stress and piezoresistive effects are provided. The electro-thermal effect initiates and thus affects electronic and mechanical characteristics of the sensor devices at high temperatures. Experiments on variation and characterization of micro-heater resistance, power consumption, thermal imaging, deformation interferometry and dynamic thermal response of the SOI micro-hotplate have been presented and discussed. The full integration of the smart gas sensor with automatically temperature-reading ICs demonstrates the lowest power consumption of 57 mW at 300 °C and fast thermal response of 10 ms. © 2008 IOP Publishing Ltd.

A resonant micromachined electrostatic charge sensor

A micromachined electrometer, based on the concept of a variable capacitor, has been designed, modeled, fabricated, and tested. The device presented in this paper functions as a modulated variable capacitor, wherein a dc charge to be measured is up-modulated and converted to an ac voltage output, thus improving the signal-to-noise ratio. The device was fabricated in a commercial standard SOI micromachining process without the need for any additional processing steps. The electrometer was tested in both air and vacuum at room temperature. In air, it has a charge-to-voltage conversion gain of 2.06 nV/e, and a measured charge noise floor of 52.4 e/rtHz. To reduce the effects of input leakage current, an electrically isolated capacitor has been introduced between the variable capacitor and input to sensor electronics. Methods to improve the sensitivity and resolution are suggested while the long-term stability of these sensors is modeled and discussed. © 2006 IEEE.

Ultrasensitive mass balance based on a bulk acoustic mode single-crystal silicon resonator

A single-crystal silicon resonant bulk acoustic mass sensor with a measured resolution of 125 pg cm2 is presented. The mass sensor comprises a micromachined silicon plate that is excited in the square-extensional bulk acoustic resonant mode at a frequency of 2.182 MHz, with a quality factor exceeding 106. The mass sensor has a measured mass to frequency shift sensitivity of 132 Hz cm2 μg. The resonator element is embedded in a feedback loop of an electronic amplifier to implement an oscillator with a short term frequency stability of better than 7 ppb at an operating pressure of 3.8 mTorr. © 2007 American Institute of Physics.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.