5.4-MHz single-crystal silicon wine glass mode disk resonator with quality factor of 2 million

This paper reports the design and electrical characterization of a micromechanical disk resonator fabricated in single crystal silicon using a foundry SOI micromachining process. The microresonator has been selectively excited in the radial extensional and the wine glass modes by reversing the polarity of the DC bias voltage applied on selected drive electrodes around the resonant structure. The quality factor of the resonator vibrating in the radial contour mode was 8000 at a resonant frequency of 6.34 MHz at pressure below 10 mTorr vacuum. The highest measured quality factor of the resonator in the wine glass resonant mode was 1.9 × 106 using a DC bias voltage of 20 V at about the same pressure in vacuum; the resonant frequency was 5.43 MHz and the lowest motional resistance measured was approximately 17 kΩ using a DC bias voltage of 60 V applied across 2.7 μm actuation gaps. This corresponds to a resonant frequency-quality factor (f-Q) product of 1.02 × 1013, among the highest reported for single crystal silicon microresonators, and on par with the best quartz crystal resonators. The quality factor for the wine glass mode in air was approximately 10,000. © 2009 Elsevier B.V. All rights reserved.

Low loss HF band SOI wine glass bulk mode capacitive square-plate resonator

This paper reports on the design and electrical characterization of a single crystal silicon micromechanical square-plate resonator. The microresonator has been excited in the anti-symmetrical wine glass mode at a resonant frequency of 5.166 MHz and exhibits an impressive quality factor (Q) of 3.7 × 106 at a pressure of 33 mtorr. The device has been fabricated in a commercial foundry process. An associated motional resistance of approximately 50 kΩ using a dc bias voltage of 60 V is measured for a transduction gap of 2 νm due to the ultra-high Q of the resonator. This result corresponds to a frequency-Q product of 1.9 × 1013, the highest reported for a fundamental mode single-crystal silicon resonator and on par with some of the best quartz crystal resonators. The results are indicative of the superior performance of silicon as a mechanical material, and show that the wine glass resonant mode is beneficial for achieving high quality factors allowed by the material limit. © 2009 IOP Publishing Ltd.

Square wine glass mode resonator with quality factor of 4 million

We report on the experimental characterization of a single crystal silicon square-plate microresonator. The resonator is excited in the square wine glass (SWG) mode at a mechanical resonance frequency of 2.065 MHz. The resonator displays quality factor of 9660 in air and an ultra-high quality factor of Q = 4.05 × 106 in 12 mtorr vacuum. The SWG mode may be described as a square plate that contracts along one axis in the fabrication plane, while simultaneously extending along an orthogonal axis in the same plane. The resonant structure is addressed in a 2-terminal configuration by utilizing equal and opposite drive polarities on surrounding capacitor electrodes, thereby decreasing the motional resistance of the resonator. The resonant micromechanical device has been fabricated in a commercial silicon-on-insulator process through the MEMSCAP foundry utilising a minimum electrostatic gap of 2 μm. © 2008 IEEE.

Sequential decision making in general state space models

Many problems in control and signal processing can be formulated as sequential decision problems for general state space models. However, except for some simple models one cannot obtain analytical solutions and has to resort to approximation. In this thesis, we have investigated problems where Sequential Monte Carlo (SMC) methods can be combined with a gradient based search to provide solutions to online optimisation problems. We summarise the main contributions of the thesis as follows. Chapter 4 focuses on solving the sensor scheduling problem when cast as a controlled Hidden Markov Model. We consider the case in which the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. In sensor scheduling, our aim is to minimise the variance of the estimation error of the hidden state with respect to the action sequence. We present a novel SMC method that uses a stochastic gradient algorithm to find optimal actions. This is in contrast to existing works in the literature that only solve approximations to the original problem. In Chapter 5 we presented how an SMC can be used to solve a risk sensitive control problem. We adopt the use of the Feynman-Kac representation of a controlled Markov chain flow and exploit the properties of the logarithmic Lyapunov exponent, which lead to a policy gradient solution for the parameterised problem. The resulting SMC algorithm follows a similar structure with the Recursive Maximum Likelihood(RML) algorithm for online parameter estimation. In Chapters 6, 7 and 8, dynamic Graphical models were combined with with state space models for the purpose of online decentralised inference. We have concentrated more on the distributed parameter estimation problem using two Maximum Likelihood techniques, namely Recursive Maximum Likelihood (RML) and Expectation Maximization (EM). The resulting algorithms can be interpreted as an extension of the Belief Propagation (BP) algorithm to compute likelihood gradients. In order to design an SMC algorithm, in Chapter 8 uses a nonparametric approximations for Belief Propagation. The algorithms were successfully applied to solve the sensor localisation problem for sensor networks of small and medium size.