TCAD device modelling and simulation of wide bandgap power semiconductors

Technology computer-aided Design (TCAD) is essential for devices technology development, including wide bandgap power semiconductors. However, most TCAD tools were originally developed for silicon and their performance and accuracy for wide bandgap semiconductors is contentious. This chapter will deal with TCAD device modelling of wide bandgap power semiconductors. In particular, modelling and simulating 3C- and 4H-Silicon Carbide (SiC), Gallium Nitride (GaN) and Diamond devices are examined. The challenges associated with modelling the material and device physics are analyzed in detail. It also includes convergence issues and accuracy of predicted performance. Modelling and simulating defects, traps and the effect of these traps on the characteristics are also discussed

Subclass Error Correcting Output Codes Using

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Subclass error correcting output codes using fisher's linear discriminant ratio

Error-Correcting Output Codes (ECOC) with subclasses reveal a common way to solve multi-class classification problems. According to this approach, a multiclass problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However, FQMI is not applicable on large datasets due to its high algorithmic complexity. In this paper we propose Fisher's Linear Discriminant Ratio (FLDR) as an alternative decomposition criterion which is of much less computational complexity and achieves in most experiments conducted better classification performance. Furthermore, we compare FLDR against FQMI for facial expression recognition over the Cohn-Kanade database. © 2010 IEEE

Optimizing subclass discriminant error correcting output codes using particle swarm optimization

Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work we show that by optimizing these thresholds via particle swarm optimization we improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers we can optimize in the same time both the thresholds of sub-ECOC and the parameters C and φ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance. © 2010 IEEE

Graph Embedded Nonparametric Mutual Information For Supervised Dimensionality Reduction

In this paper, we propose a novel algorithm for dimensionality reduction that uses as a criterion the mutual information (MI) between the transformed data and their cor- responding class labels. The MI is a powerful criterion that can be used as a proxy to the Bayes error rate. Further- more, recent quadratic nonparametric implementations of MI are computationally efficient and do not require any prior assumptions about the class densities. We show that the quadratic nonparametric MI can be formulated as a kernel objective in the graph embedding framework. Moreover, we propose its linear equivalent as a novel linear dimensionality reduction algorithm. The derived methods are compared against the state-of-the-art dimensionality reduction algorithms with various classifiers and on various benchmark and real-life datasets. The experimental results show that nonparametric MI as an optimization objective for dimensionality reduction gives comparable and in most of the cases better results compared with other dimensionality reduction methods

Laplacian Support Vector Analysis for Subspace Discriminative Learning

In this paper we propose a novel dimensionality reduction method that is based on successive Laplacian SVM projections in orthogonal deflated subspaces. The proposed method, called Laplacian Support Vector Analysis, produces projection vectors, which capture the discriminant information that lies in the subspace orthogonal to the standard Laplacian SVMs. We show that the optimal vectors on these deflated subspaces can be computed by successively training a standard SVM with specially designed deflation kernels. The resulting normal vectors contain discriminative information that can be used for feature extraction. In our analysis, we derive an explicit form for the deflation matrix of the mapped features in both the initial and the Hilbert space by using the kernel trick and thus, we can handle linear and non-linear deflation transformations. Experimental results in several benchmark datasets illustrate the strength of our proposed algorithm

Carrier Transport mechanisms contributing to the sub-threshold current in 3C-SiC-on-Si Schottky Barrier Diodes

3C-Silicon Carbide (3C-SiC) Schottky Barrier Diodes on silicon (Si) substrates (3C-SiC-on-Si) seem not to comply with the superior wide band gap expectations in terms of excessive measured sub-threshold current. In turn, that is one of the factors which deters their commercialization. Interestingly, the forward biased part of the Current-Voltage (I-V) characteristics in these devices carries considerable information about the material quality. In this context, an advanced Technology Computer Aided Design (TCAD) model for a vertical Platinum/3C-SiC Schottky power diode is created and validated with measured data. The model includes defects originating from both the Schottky contact and the hetero-interface of 3C-SiC with Si which allows the investigation of their impact on the magnification of the sub-threshold current. For this, barrier lowering, quantum field emission and trap assisted tunneling of majority carriers need to be considered at the non-ideal Schottky interface. The simulation results and measured data allowed for the comprehensive characterization of the defects affecting the carrier transport mechanisms of the forward biased 3C-SiC on Si power rectifier for the first time