Analysis on the off-state design and characterization of LIGBTs in partial SOI technology

Classical high voltage devices fabricated on SOI substrates suffer from a backside coupling effect which could result in premature breakdown. This phenomenon becomes more prominent if the structure is an IGBT which features a p-type injector. To suppress the premature breakdown due to crowding of electro-potential lines within a confined SOI/buried oxide structure, the partial SOI (PSOI) technique is being introduced. This paper analyzes the off-state behavior of an n-type Superjunction (SJ) LIGBT fabricated on PSOI substrate. During the initial development stage the SJ LIGBT was found to have very high leakage. This was attributed to the back and side coupling effects. This paper discusses these effects and shows how this problem could be successfully addressed with minimal modifications of device layout. The off-state performance of the SJ LIGBT at different temperatures is assessed and a comparison to an equivalent LDMOSFET is given. © 2014 Elsevier Ltd. All rights reserved.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

We develop methods for performing filtering and smoothing in non-linear non-Gaussian dynamical models. The methods rely on a particle cloud representation of the filtering distribution which evolves through time using importance sampling and resampling ideas. In particular, novel techniques are presented for generation of random realisations from the joint smoothing distribution and for MAP estimation of the state sequence. Realisations of the smoothing distribution are generated in a forward-backward procedure, while the MAP estimation procedure can be performed in a single forward pass of the Viterbi algorithm applied to a discretised version of the state space. An application to spectral estimation for time-varying autoregressions is described.

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.

Crystal growth of melt-textured Nd-123 under 1% oxygen partial pressure atmosphere

Crystal growth of melt-textured Nd-123 pseudo-crystals was investigated via an isothermal solidification with top-seeding technique under a 1%O2 in N2 atmosphere. Non-steady state solidification was observed at low undercooling, in contrast to an almost linear growth at higher undercooling. Similar to processing in air, the substitution of Nd/Ba was found to decrease from the seed position to the edge of the crystal. In addition, the volume fraction of Nd-422 particles decreased in the solid as solidification proceeded. As a result of these microstructural inhomogeneities, the critical temperature and the critical current density varied within the crystal even for samples processed isothermally, despite the narrow solid solution range of the Nd-123 phase under a reduced pO2 atmosphere.

Viscoelastic properties of bone as a function of hydration state determined by nanoindentation

Spherical indentation creep testing was used to examine the effect of hydration state on bone mechanical properties. Analysis of creep data was based on the elastic-viscoelastic correspondence principle and utilized a direct solution for the finite loading-rate experimental conditions. The zero-time shear modulus was computed from the creep compliance function and compared to the indentation modulus obtained via conventional indentation analysis, based on an elastic unloading response. The method was validated using a well-known polymer material under three different loading conditions. The method was applied to bone samples prepared with different water content by partial exchange with ethanol, where 70% ethanol was considered as the baseline condition. A hydration increase was associated with a 43% decrease in stiffness, while a hydration decrease resulted in a 20% increase in bone tissue stiffness.

Multiple pitch estimation using non-homogeneous poisson processes

Novel statistical models are proposed and developed in this paper for automated multiple-pitch estimation problems. Point estimates of the parameters of partial frequencies of a musical note are modeled as realizations from a non-homogeneous Poisson process defined on the frequency axis. When several notes are combined, the processes for the individual notes combine to give a new Poisson process whose likelihood is easy to compute. This model avoids the data-association step of linking the harmonics of each note with the corresponding partials and is ideal for efficient Bayesian inference of unknown multiple fundamental frequencies in a signal. © 2011 IEEE.