Bias‐dependent small‐signal modeling based on neuro‐space mapping for MOSFET

In this article, bias‐dependent small‐signal modeling approach based on neuro‐space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz–40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Genetic algorithm‐based neural‐network modeling approach applied to AlGaN/GaN devices

An accurate equivalent circuit large‐signal model (ECLSM) for AlGaN‐GaN high electron mobility transistor (HEMT) is presented. The model is derived from a distributed small‐signal model that efficiently describes the physics of the device. A genetic neural‐network‐based model for the gate and drain currents and charges is presented along with its parameters extraction procedure. This model is embedded in the ECLSM, which is then implemented in CAD software and validated by pulsed and continuous large‐signal measurements of on‐wafer 8 × 125‐μm GaN on SiC substrate HEMT. Pulsed IV simulations show that the model can efficiently describe the bias dependency of trapping and self‐heating effects. Single‐ and two‐tone simulation results show that the model can accurately predict the output power and its harmonics and the associated intermodulation distortion (IMD) under different input‐power and bias conditions. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

Neural inverse space mapping (NISM) optimization for EM‐based microwave design

We present neural inverse space mapping (NISM) optimization for electromagnetics‐based design of microwave structures. The inverse of the mapping from the fine to the coarse model parameter spaces is exploited for the first time in a space mapping algorithm. NISM optimization does not require up‐front EM simulations, multipoint parameter extraction, or frequency mapping. It employs a simple statistical parameter extraction procedure. The inverse of the mapping is approximated by a neural network whose generalization performance is controlled through a network growing strategy. We contrast our new algorithm with neural space mapping (NSM) optimization. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 136–147, 2003.     

Application of neural networks in space‐mapping optimization of microwave filters

In this paper, a design methodology combining coupling matrix representation of filters, neural models and space‐mapping techniques is presented for further enhancement of optimization efficency of microwave filters. Neural models are developed for both initial dimension generation and design parameter sensitivity analysis. Combining neural models of filter substructures with space‐mapping optimization, the total number of EM simulations of the complete filter structure is significantly reduced. The improvement in efficiency over conventional method is demonstrated using simulation and measurement results of both end‐coupled and side‐coupled waveguide dual‐mode pseudo‐elliptic filters. The total CPU times for design and optimization are reduced by 50% to 70 %.© 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2012.     

EM‐based yield optimization exploiting trust‐region optimization and space mapping technology

Design centering is a design problem which looks for nominal values of circuit parameters that maximize the probability of satisfying the design specification (yield function). Direct yield optimization of electromagnetic (EM)‐based circuits is obstructed by the high expense of EM simulations required in the yield estimation process. Also, the absence of any gradient information represents an obstacle against the optimization process. In this article, a new approach for design centering and yield optimization of EM‐based circuits is introduced. In the proposed approach, the generalized space mapping (SM) technique is incorporated with a derivative‐free trust region optimization method (NEWUOA). Moreover, a variance reduction sampling technique is implemented in the yield estimation process. Two techniques suitable for the microwave circuit design centering process are introduced. The first technique exploits the surrogate developed using any circuit optimizer, for example, minimax optimizer, in the yield maximization process. While the second technique iteratively constructs and updates an SM surrogate during the yield optimization process. Our novel approach is illustrated by practical examples showing its efficiency. One of the examples is entirely designed within the sonnet em environment. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:474–484, 2015.     

Synthesis of slow‐wave structures based on capacitive‐loaded lines through aggressive space mapping (ASM)

This article is focused on the automated synthesis of slow‐wave structures based on microstrip lines loaded with patch capacitors. Thanks to the presence of the shunt capacitors, the effective capacitance of the line is enhanced, and the phase velocity of the structure can be made significantly smaller than the one of the unloaded line. The target is to achieve the layout of the slow‐wave structure able to provide the required slow‐wave ratio, characteristic (Bloch) impedance and electrical length (i.e., the usual specifications in the design of slow‐wave transmission lines). To this end, a two‐step synthesis method, based on the aggressive space mapping (ASM) algorithm, is proposed for the first time. Through the first ASM algorithm, the circuit schematic providing the target specifications is determined. Then, the second ASM optimizer is used to generate the layout of the structure. To illustrate the potential of the proposed synthesis method, three application examples are successfully reported. The two‐step ASM algorithm is able to provide the layout of the considered structures from the required specifications, without the need of an external aid in the process. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:629–638, 2015.     

Reliable design optimization of microwave structures using multipoint‐response‐correction space mapping and trust regions

Space mapping (SM) is one of the most efficient simulation‐driven design technologies used in microwave engineering to date. It includes so‐called output SM that ensures exact matching between the EM‐evaluated microwave structure under consideration (fine model) and its surrogate at the current design. The standard, single‐point output SM exploits the fine model data at a single design and is not able to align the models' sensitivity. Here, a multipoint response correction is proposed that generalizes the concept of output SM. By using a design‐variable‐dependent correction term and exploiting all available fine model information, the proposed technique provides exact match between the surrogate and the fine model at several designs. This retains the benefits of output SM but also enhances sensitivity matching between the two models, which results in improved performance of the SM optimization process. The efficiency of the propose approach is demonstrated using several microwave design problems. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Design and optimization of cross‐coupled substrate integrated waveguide filters using space mapping method

This work presents an efficient method for the design of substrate integrated waveguide (SIW) filters. The proposed design approach is based on a combined use of equivalent circuit model of a filter and a space mapping technique. A reduced number of full‐wave evaluations are needed, leading to a reduced optimization time. A novel SIW filter with improved stop‐band characteristic using cross‐coupling has been proposed. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:360–366, 2014.     

Experimental decomposition of the positive bias temperature stress‐induced instability in self‐aligned coplanar InGaZnO thin‐film transistors and its modeling based on the multiple stretched‐exponential functions

Decomposition of the positive gate‐bias temperature stress (PBTS)‐induced instability into contributions of distinct mechanisms is experimentally demonstrated at several temperatures in top‐gate self‐aligned coplanar amorphous InGaZnO thin‐film transistors by combining the stress‐time‐divided measurements and the subgap density‐of‐states (DOS) extraction. It is found that the PBTS‐induced threshold voltage shift (ΔV_T) consists of three mechanisms: (1) increase of DOS due to excess oxygen in the active region; (2) shallow; and (3) deep charge trapping in the gate insulator components. Corresponding activation energy is 0.75, 0.4, and 0.9 eV, respectively. The increase of DOS is physically identified as the electron‐capture by peroxide. Proposed decomposition is validated by reproducing the PBTS time‐evolution of I–V characteristics through the technology computer‐aided design simulation into which the extracted DOS and charge trapping are incorporated. It is also found that the quantitative decomposition of PBTS‐induce ΔV_T accompanied with the multiple stretched‐exponential models enables an effective assessment of the complex degradation nature of multiple PBTS physical processes occurring simultaneously. Our results can be easily applied universally to any device with any stress conditions, along with guidelines for process optimization efforts toward ultimate PBTS stability.     

Experimental insight into the temperature effects on DC and microwave characteristics for a GaAs pHEMT in multilayer 3‐D MMIC technology

This paper is focused on studying the behavior of a GaAs pseudomorphic high electron mobility transistors (pHEMT) with respect to the temperature. The tested pHEMT is realized using the multilayer three‐dimensional (3‐D) monolithic microwave integrated circuit (MMIC) technology. The analysis is based on temperature‐dependent on‐wafer measurements carried out from 298 K to 373 K. The experiments consist of DC characteristics and scattering parameters in the broad frequency range from 45 MHz to 40 GHz. The effect of the temperature on the measured transistor performance is analyzed in detail and then, to gain a better insight and understanding of the device behavior, the achieved measurements are used for extraction and validation of a small‐signal equivalent‐circuit model for different temperature conditions. This study shows that, by heating the studied device, the observed performance variations depend remarkably on the selected bias condition. In particular, the output current and transconductance are degraded at higher gate‐source voltage and improved as the transistor is driven towards the pinch‐off. This is due to the counterbalancing of temperature‐dependent effects contributing in opposite ways to the resultant behavior of the transistor. Therefore, depending on the given application, an appropriate selection of the bias and temperature conditions is essential to guarantee adequate transistor performance.     

Real‐Time Results for High Order Neural Identification and Block Control Transformation Form Using High Order Sliding Modes

In this paper, real‐time results for a novel continuous‐time adaptive tracking controller algorithm for nonlinear multiple input multiple output systems are presented. The control algorithm includes the combination of a recurrent high order neural network with block control transformation using a high order sliding modes technique as control law. A neural network is used to identify the dynamic plant behavior where a filtered error algorithm is used to train the neural identifier. A decentralized high order sliding mode, named the twisting algorithm, is used to design chattering‐reduced independent controllers to solve the trajectory tracking problem for a robot arm with three degrees of freedom. Stability analyses are given via a Lyapunov approach.     

Robust Kalman filter and smoother for errors‐in‐variables state space models with observation outliers based on the minimum‐covariance determinant estimator

In this paper, we propose a robust Kalman filter and smoother for the errors‐in‐variables (EIV) state space models subject to observation noise with outliers. We introduce the EIV problem with outliers and then present the minimum covariance determinant (MCD) estimator which is a highly robust estimator in terms of protecting the estimate from the outliers. Then, we propose the randomized algorithm to find the MCD estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimates, therefore we apply the sub‐sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

A comparative study on a novel model‐based PID tuning and control mechanism for nonlinear systems

This work presents a novel predictive model‐based proportional integral derivative (PID) tuning and control approach for unknown nonlinear systems. For this purpose, an NARX model of the plant to be controlled is obtained and then it used for both PID tuning and correction of the control action. In this study, for comparison, neural networks (NNs) and support vector machines (SVMs) have been used for modeling. The proposed structure has been tested on two highly nonlinear systems via simulations by comparing control and convergence performances of SVM‐ and NN‐Based PID controllers. The simulation results have shown that when used in the proposed scheme, both NN and SVM approaches provide rapid parameter convergence and considerably high control performance by yielding very small transient‐ and steady‐state tracking errors. Moreover, they can maintain their control performances under noisy conditions, while convergence properties are deteriorated to some extent due to the measurement noises. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

This work aims at developing an explicit neuro‐fuzzy (NF) model to characterize complex engineered systems associated with high nonlinearity, uncertainties, and multivariable couplings. The NF model synergistically exploits the advantages of fuzzy belongingness of each input variable to all output variables and learning ability of neural networks. Owing to the inherent complexities associated with 2 complex engineered systems, a landfill and a boiler were selected to develop models that provide intelligent decisions for optimizing the operational parameters. Data compiled from field‐scale investigation/real plant operation involving various operating scenarios were used to develop the models. Predicting capability of the developed models was evaluated through the correlation coefficient and mean absolute percentage error values. Superiority of the proposed NF model to other similar models has been justified and demonstrated.