Systematic characterization of Cl2 reactive ion etchingfor improved ohmics in AlGaN/GaN HEMTs

Pre-metal-deposition reactive ion etching (RIE) was performed on an Al_0.3Ga_0.7N/AlN/GaN heterostructure in order to improve the metal-to-semiconductor contact resistance. An optimum AlGaN thickness for minimizing contact resistance was determined. An initial decrease in contact resistance with etching time was explained in terms of removal of an oxide surface layer and/or by an increase in tunnelling current with the decrease of the AlGaN thickness. The presence of a dissimilar surface layer was confirmed by an initial nonuniform etch depth rate. An increase in contact resistance for deeper etches was experienced. The increase was related to depletion of the two-dimensional (2-D) electron gas (2-DEG) under the ohmics. Etch depths were measured by atomic force microscopy (AFM). The contact resistance decreased from about 0.45 Ωmm for unetched ohmics to a minimum of 0.27 Ωmm for 70 Å etched ohmics. The initial thickness of the AlGaN layer was 250 Å. The decrease in contact resistance, without excessive complications on device processing, supports RIE etching as a viable solution to improve ohmic contact resistance in AlGaN/GaN HEMTs     

A random switching method for HPWM full-bridge inverter

To overcome the problem of unequal switching loss in power switches, in conventional hybrid pulse width modulation (HPWM) full-bridge inverters, a random switching method for HPWM full-bridge inverters is proposed. The proposed method equalizes switching losses of the four switches, while also providing good output performance     

Image processing using cellular neural networks based on multi-valued and universal binary neurons

Multi-valued and universal binary neurons (MVN and UBN) are the neural processing elements with the complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by partially defined multiple-valued function on the single MVN. An arbitrary mapping described by partially defined or fully defined Boolean function, which can be non-threshold, may be implemented on the single UBN. The quickly converging learning algorithms exist for both types of neurons. Such features of the MVN and UBN may be used for solving the different problems. One of the most successful applications of the MVN and UBN is their usage as basic neurons in the Cellular Neural Networks (CNN). It opens the new effective opportunities in nonlinear image filtering and its applications to noise reduction, edge detection and solving of the super resolution problem. A number of experimental results are presented to illustrate the performance of the proposed algorithms.An erratum to this article can be found at     

Magnetic field dependence of shielding current density in Y-Ba-Cu-O rings at 77 K

A method for contactless measurement of the shielding critical current density and its dependence on the external magnetic field is described and analyzed. The obtained values are compared with those measured resistively on two different samples. It is shown that the shielding critical current densityJ _cs and the intergranular transport current densityJ _cr are identical if the measurement conditions are similar. A degradation ofJ _cs measured in the external field with AC ripple has been observed.     

A robust adaptive pole‐placement controller without strictly positive real condition

This paper considers the adaptive pole‐placement control problem for system (1) with unmodelled dynamics η_n dominated by a small constant ε multiplied by a quantity independent of ε but tending to infinity as the past input, output, and noise grow. Using bounded external excitation and randomly varying truncation techniques, we give a design method of adaptive pole‐placement controller. It is shown that the closed‐loop system is globally stable, the estimation error for the parameter contained in the modelled part is of order ε, and the closed‐loop system under the adaptive pole‐placement control law is suboptimal in the sense of $$\mathop{\lim\sup}\limits_{{n\to\infty }}{1\over n}\mathop{\sum}\limits_{i=0}^n{\left({A^{*}(z)y_{n}‐L(z)C(z)w_{n}‐B(z)R(z)y_{n}^{*}}\right)^{2}{\leq}O({\varepsilon}^{2})+\gamma^{2}\mathop{\sum}\limits_{j=1}^q{b_{j}^{2}}}$$\nopagenumbers\end while the SPR condition used usually in other papers is replaced by a stability condition. Copyright © 2001 John Wiley & Sons, Ltd.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.