Steady-state analytical model for the trench insulated gate bipolar transistor

A steady-state, physically-based analytical model for the Trench Insulated Gate Bipolar Transistor which accounts for a combined PIN diode - PNP transistor carrier dynamics is proposed. Previous models (i.e. PIN model and PNP transistor model) cannot account properly for the carrier dynamics in Trench IGBT since neither the PNP transistor nor the PIN diode effect can be neglected. An optimized Trench IGBT with a large ratio between the accumulation layer and the cell size leads to substantially improved on-state characteristics, which makes the Trench IGBT potentially the most attractive device in the area of high voltage fast switching devices.

Unexpected controllable pair-structure in ferroelectric nanodomains.

The imminent inability of silicon-based memory devices to satisfy Moore's Law is approaching rapidly. Controllable nanodomains of ferroic systems are anticipated to enable future high-density nonvolatile memory and novel electronic devices. We find via piezoresponse force microscopy (PFM) studies on lead zirconate titanate (PZT) films an unexpected nanostructuring of ferroelectric-ferroelastic domains. These consist of c-nanodomains within a-nanodomains in proximity to a-nanodomains within c-domains. These structures are created and annihilated as pairs, controllably. We treat these as a new kind of vertex-antivertex pair and consider them in terms of the Srolovitz-Scott 4-state Potts model, which results in pairwise domain vertex instabilities that resemble the vortex-antivortex mechanism in ferromagnetism, as well as dislocation pairs (or disclination pairs) that are well-known in nematic liquid crystals. Finally, we show that these nanopairs can be scaled up to form arrays that are engineered at will, paving the way toward facilitating them to real technologies.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, are numerical techniques based on Importance Sampling for solving the optimal state estimation problem. The task of calibrating the state-space model is an important problem frequently faced by practitioners and the observed data may be used to estimate the parameters of the model. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed for this task accompanied with a discussion of their advantages and limitations.

Transient thermal analysis of power devices based on Fourier-series thermal model

A new thermal model based on Fourier series expansion method has been presented for dynamic thermal analysis on power devices. The thermal model based on the Fourier series method has been programmed in MATLAB SIMULINK and integrated with a physics-based electrical model previously reported. The model was verified for accuracy using a two-dimensional Fourier model and a two-dimensional finite difference model for comparison. To validate this thermal model, experiments using a 600V 50A IGBT module switching an inductive load, has been completed under high frequency operation. The result of the thermal measurement shows an excellent match with the simulated temperature variations and temperature time-response within the power module. ©2008 IEEE.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

A single-rate context-dependent learning process underlies rapid adaptation to familiar object dynamics.

Motor learning has been extensively studied using dynamic (force-field) perturbations. These induce movement errors that result in adaptive changes to the motor commands. Several state-space models have been developed to explain how trial-by-trial errors drive the progressive adaptation observed in such studies. These models have been applied to adaptation involving novel dynamics, which typically occurs over tens to hundreds of trials, and which appears to be mediated by a dual-rate adaptation process. In contrast, when manipulating objects with familiar dynamics, subjects adapt rapidly within a few trials. Here, we apply state-space models to familiar dynamics, asking whether adaptation is mediated by a single-rate or dual-rate process. Previously, we reported a task in which subjects rotate an object with known dynamics. By presenting the object at different visual orientations, adaptation was shown to be context-specific, with limited generalization to novel orientations. Here we show that a multiple-context state-space model, with a generalization function tuned to visual object orientation, can reproduce the time-course of adaptation and de-adaptation as well as the observed context-dependent behavior. In contrast to the dual-rate process associated with novel dynamics, we show that a single-rate process mediates adaptation to familiar object dynamics. The model predicts that during exposure to the object across multiple orientations, there will be a degree of independence for adaptation and de-adaptation within each context, and that the states associated with all contexts will slowly de-adapt during exposure in one particular context. We confirm these predictions in two new experiments. Results of the current study thus highlight similarities and differences in the processes engaged during exposure to novel versus familiar dynamics. In both cases, adaptation is mediated by multiple context-specific representations. In the case of familiar object dynamics, however, the representations can be engaged based on visual context, and are updated by a single-rate process.

Thermo-hydro-geomechanical simulations of methane gas production from deep sea methane hydrate formations

A new constitutive model called Methane Hydrate Critical State (MHCS) model was conducted to investigate the geomechanical response of the gas-hydrate-bearing sediments at the Nankai Trough during the wellbore construction process. The strength and dilatancy of gas-hydrate-bearing soil would gradually disappear when the bonds are destroyed because of excessively shearing, which are often observed in dense soils and also in bonded soils such as cemented soil and unsaturated soil. In this study, the MHCS model, which presents such softening features, would be incorporated into a staged-finite-element model in ABAQUS, which mainly considered the loading history of soils and the interaction between cement-casing-formation. This model shows the influence of gas-hydrate-bearing soil to the deformation and stability of a wellbore and the surrounding sediments during wellbore construction. At the same time, the conventional Mohr-Coulomb model was used in the model to show the advantages of MHCS model by comparing the results of the two models.