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The nonlinearities and memory effects of power amplifiers (PAs) can be compensated by multistage cascaded digital predistortion with low complexity. Compared with full multistage models, sparse multistage models may have the same linearization performances while their complexities are even lower. However, the choice of the model structure is very difficult, especially for the sparse models. For instance, if there are $(K+1)(L+1)$ full models, then $2^{K+L-2}$ different corresponding sparse models exist. An algorithm with the optimal search space definition is proposed in this paper to search for the optimal cascaded sparse model structure. The search criterion represents tradeoff between the modeling accuracy and identification complexity with a weight coefficient. A method to determine the value of the weight coefficient is proposed in this paper. The sparse model solution found by the proposed algorithm is evaluated with a three-way Doherty PA using a long term evolution-advanced signal. It is also compared with the solution of full model structure search. [ABSTRACT FROM AUTHOR]

In this paper, compact time-domain (TD) models featuring materials with frequency-dependent electromagnetic (EM) properties are derived. The considered frequency-dependent material models include multiterm Debye and Lorentz models for the electric permittivity and the magnetic permeability and a multiterm Drude model for the electric conductivity. The TD models are based on finite-element systems in the frequency domain (FD). To render the model compact and computationally efficient, the dimension of the FD system is compressed with the help of projection-based model-order reduction. In contrast to older approaches, the TD transformation is performed on the FD model itself rather than on the transfer function. The result is a state-space representation, which may either be solved by custom time integrators or imported into commercial circuit simulators. The advantages of the new approach include provable passivity of the FD model, provable causality of the TD model, and the ability to reconstruct the transient EM fields. [ABSTRACT FROM AUTHOR]

An enhanced full-wave method based on circuit analysis is presented in this paper, where the whole set of modes TEmnp and TMmnp are taken into account. The modeling of a split cylinder resonator is done with two circuit networks of one and three ports, characterized by their generalized admittance matrix, which is computed making use of the mode matching method. The improved full-wave circuit method has been applied to the accurate determination of dielectric properties of materials. The proposed method has been validated through comparisons with other published models and also with measurements. [ABSTRACT FROM PUBLISHER]

Knowledge-based neural network modeling techniques using space-mapping concept have been demonstrated in the existing literature as efficient methods to overcome the accuracy limitations of empirical/equivalent circuit models when matching new electromagnetic data. For different modeling problems, the mapping structures can be different. In this paper, we propose a unified automated model generation algorithm that uses \boldsymbol l\mathbf {1} optimization to automatically determine the type and the topology of the mapping structure in a knowledge-based neural network model. By encompassing various types of mappings of the knowledge-based neural network model in the existing literature, we present a new unified model structure and derive new sensitivity formulas for the training of the unified model. The proposed \boldsymbol l\mathbf {1} formulation of modeling can force some weights of the mapping neural networks to zeros while leaving other weights as nonzeros. We utilize this feature to allow \boldsymbol l\mathbf {1} optimization to automatically determine which mapping is necessary and which mapping is unnecessary. Using the proposed \boldsymbol l\mathbf {1} optimization method, the mapping structure can be determined to address different needs of different modeling problems. The structure of the final knowledge-based model can be flexible combinations of some or all of linear mapping, nonlinear mapping, input mapping, frequency mapping, and output mapping. In this way, the proposed algorithm is more systematic and can further speed up the knowledge-based modeling process than existing knowledge-based modeling algorithms. The proposed method is illustrated by three microwave filter modeling examples. [ABSTRACT FROM PUBLISHER]

This paper presents a comparison of existing and novel behavioral models targeted at the outphasing power amplifier (PA) architecture. A comprehensive comparison of ten modeling strategies is presented in the results. Novel techniques for outphasing PAs, such as vector switched and dual path time series, are also presented for the first time. Investigation of such techniques was driven by the analysis of outphasing operation at minimum output powers, demonstrating the generation of frequency-dependent amplitude and phase deviations, which can be difficult to characterize. The increased robustness was achieved at the cost of additional complexity; for practical implementation, time series coefficient reduction techniques were also evaluated. The results of all modeling approaches are experimentally validated for the wideband operation of an NXP 19-W GaN digital outphasing amplifier module. Considering computational complexity and accuracy for system-level modeling across all presented options, a subset of existing and new models is identified as best suited for modeling outphasing PAs. [ABSTRACT FROM PUBLISHER]

Measurement-based black box behavioral models are widely used nowadays. To handle nonlinear effects efficiently, such models often rely on approximation techniques. Polyharmonic distortion (PHD) modeling emerged as a viable approach for describing a nonlinear mapping. Typical PHD-based models, such as the well-known $X$ -parameter model, are gained from linearization while operating on a certain large signal (LS) operational point. This limits the accuracy, especially for hard nonlinearities. However, quadratic terms can be added, which result in the quadratic PHD (QPHD) model. This enables highly accurate models for devices in strongly nonlinear operation, even in highly mismatched environments. In this paper, the accuracy of such models is investigated by predicting typical nonlinear measures, such as load–pull contours and intermodulation distortion, to assess the model accuracy for both static and dynamic stimulus. Furthermore, the LS matching problem is solved for both the $X$ -parameter and the QPHD model. This allows to predict the optimum matching analytically, without performing load–pull analysis. To verify the accuracy of the model, the results are presented by comparing the model prediction with verification measurements for a commercially available GaN HEMT. [ABSTRACT FROM AUTHOR]

Space mapping is a recognized method for speeding up electromagnetic (EM) optimization. Existing space-mapping approaches belong to the class of surrogate-based optimization methods. This paper proposes a cognition-driven formulation of space mapping that does not require explicit surrogates. The proposed method is applied to EM-based filter optimization. The new technique utilizes two sets of intermediate feature space parameters, including feature frequency parameters and ripple height parameters. The design variables are mapped to the feature frequency parameters, which are further mapped to the ripple height parameters. By formulating the cognition-driven optimization directly in the feature space, our method increases optimization efficiency and the ability to avoid being trapped in local minima. The technique is suitable for design of filters with equal-ripple responses. It is illustrated by two microwave filter examples. [ABSTRACT FROM PUBLISHER]

Yield-driven optimization is important in microwave design due to the uncertainties introduced in the manufacturing process. For the first time, we extend in this paper the use of polynomial chaos (PC) approach from electromagnetic (EM)-based yield estimation to EM-based yield optimization of microwave structures. We first formulate a novel objective function for yield-driven EM optimization. By incorporating the PC coefficients into the formulation, the objective function is analytically related to yield optimization variables, which are the nominal point. We then derive the sensitivity formulas of the PC coefficients with respect to the nominal point, following which we derive the sensitivities of the optimization objective function with respect to yield optimization variables. These sensitivities are then used in gradient-based optimization algorithms to find the optimal yield solution iteratively. The proposed objective function requires fewer EM simulations to provide reliable yield representation than that in the conventional Monte Carlo-based yield optimization approach. As a result, the number of EM simulations required to find the update direction and suitable step size for the change of the nominal point is reduced at each iteration of optimization. This allows the proposed approach to achieve similar yield increase using much fewer EM simulations or greater yield increase using similar number of EM simulations compared to the conventional yield optimization approach. The advantages of our proposed approach are demonstrated by yield-driven EM optimization of three waveguide filter examples. [ABSTRACT FROM AUTHOR]

This paper proposes a novel technique to develop a low-cost electromagnetic (EM) centric multiphysics parametric model for microwave components. In the proposed method, we use space mapping techniques to combine the computational efficiency of EM single physics (EM only) simulation with the accuracy of the multiphysics simulation. The EM responses with respect to different values of geometrical parameters in nondeformed structures without considering other physics domains are regarded as coarse model. The coarse model is developed using the parametric modeling methods such as artificial neural networks or neuro-transfer function techniques. The EM responses with geometrical and nongeometrical design parameters as variables in the practical deformed structures due to thermal and structural mechanical stress factors are regarded as fine model. The fine model represents the behavior of EM centric multiphysics responses. The proposed model includes the EM domain coarse model and two mapping neural networks to map the EM domain (single physics) to the multiphysics domain. Our proposed technique can achieve good accuracy for multiphysics parametric modeling with fewer multiphysics training data and less computational cost. After the modeling process, the proposed model can be used to provide accurate and fast prediction of EM centric multiphysics responses of microwave components with respect to the changes of design parameters within the training ranges. The proposed technique is illustrated by a tunable four-pole waveguide filter example at 10.5–11.5 GHz and an iris coupled microwave cavity filter example at 690–720 MHz. [ABSTRACT FROM AUTHOR]

The use of multisine signals to improve the efficiency of wireless power transfer (WPT) for low average received power was proposed recently. Several measurement-based studies illustrated the gain that can be achieved for different circuit or waveform instances, focusing on the impact of a time-varying amplitude on the rectifying efficiency. This paper first establishes a model enabling a thorough analysis of the multisine-based WPT system focusing on the bandwidth of the signal and the rectifier. This model enables a codesign of signal and rectifier for optimal WPT. The proposed model provides insight into the output voltage and power, as a function of the input waveform for different circuit models. By including the input matching and the clamper, our model is generic and can include a wide range of rectifiers with different voltage multiplication approaches. The key insight gained from our analysis is that there is a tradeoff between the frequency spacing of the tones of the multisine signal and the cut-off frequency of the low-pass RC filter, as a main property of the rectifier circuit. Our model predicts the measured power conversion efficiency and voltage with an error below 0.1 and 0.2 V, respectively. [ABSTRACT FROM PUBLISHER]

This paper presents a polynomial chaos (PC) formulation based on the concept of dimension reduction for the efficient uncertainty analysis of microwave and RF networks. This formulation exploits a high-dimensional model representation for quantifying the relative effect of each random dimension on the network responses surface. This information acts as problem-dependent sensitivity indices guiding the intelligent identification and subsequent pruning of the statistically unimportant random dimensions from the original parametric space. Performing a PC expansion in the resultant low-dimensional random subspace leads to the recovery of a sparser set of coefficients than that obtained from the full-dimensional random space with negligible loss in accuracy. Novel methodologies to reuse the preliminary PC bases and SPICE simulations required to estimate the sensitivity indices are presented, thereby making the proposed approach more efficient and accurate than standard sparse PC approaches. The validity of the proposed approach is demonstrated using three distributed network examples. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel hardware implementation strategy to achieve low-cost design for digital predistortion of radio frequency power amplifiers (PAs) using a modified decomposed vector rotation-based behavioral model. To make the model hardware friendly, we first modify the model into a subdecomposed format, which significantly reduces the computational complexity in model extraction. We then reassemble the coefficients and propose a simple digital implementation structure for real-time signal processing in the transmit path. A new dual-direction coordinate rotation digital computer design is also proposed to simultaneously calculate both magnitude and e^j\theta n values to facilitate the model implementation. To validate hardware implementation, a wideband signal is employed to evaluate the performance with a Doherty PA. Experimental results show that the proposed approach can achieve comparable performance with much lower system complexity compared with that using the conventional approaches. [ABSTRACT FROM PUBLISHER]

Electromagnetic (EM) optimization is an essential part of an EM design cycle. In this paper, for the first time, a decomposition method is formulated to address the challenges of EM optimization with many design variables. Single large EM optimization is decomposed into multiple smaller suboptimizations to improve the optimization efficiency. In practical EM optimizations, there are no two design variables whose effect on the output response is completely independent. We propose a decomposition method, which uses the second-order derivative information to measure the level of dependence (measure of interaction) between the design variables and thereby identify, separate, and group the design variables to form multiple smaller suboptimizations. Each suboptimization includes generating multiple fine model responses for constructing the surrogate model and its optimization using trust region. Multiple suboptimizations are formulated to be independent, so that parallel computations can be exploited to evaluate concurrent suboptimization updates. The resultant vector, a combined vector of suboptimization updates, will be closer to the optimal solution. Furthermore, the step size of the optimization update in the proposed approach is much larger in comparison with the step size of the optimization update without decomposition. Using this approach, the overall large optimization update decreases the value of the objective function more efficiently and quickly. Two examples of EM optimization are used to illustrate the proposed technique. [ABSTRACT FROM PUBLISHER]

In this paper, a novel methodology for cost-efficient microwave design optimization in the frequency domain is proposed. Our technique, referred to as adaptive response scaling (ARS), has been developed for constructing a fast replacement model (surrogate) of the high-fidelity electromagnetic-simulated model of the microwave structure under design using its equivalent circuit (low-fidelity model). The basic principle of ARS is a nonlinear frequency and amplitude response scaling aimed at accommodating the discrepancies between the low- and high-fidelity models at the reference design and, subsequently, at tracking the low-fidelity model changes that occur during the optimization run. The surrogate model prediction is obtained by applying appropriately composed scaling functions to the high-fidelity model at the reference design. ARS is a parameterless and simple-to-implement method that can be applied to a wide range of microwave structures. The ARS surrogate features excellent generalization capability that translates into improved reliability and reduced design cost. It is demonstrated using an eighth-order microstrip bandpass filter and a miniaturized rat-race coupler. Comparison with several space mapping algorithms is provided. The numerical results are supplemented by measurements of the fabricated optimum designs of the considered structures. [ABSTRACT FROM AUTHOR]

In this paper, the problem of geometry scaling of compact microwave structures is investigated. As opposed to conventional structures [i.e., constructed using uniform transmission lines (TLs)], re-design of miniaturized circuits (e.g., implemented with artificial TLs) for different operating frequencies is far from being straightforward due to considerable cross-couplings between the circuit components. Here, we develop a simple and computationally efficient methodology for dimension scaling of the compact circuits. The proposed approach utilizes an equivalent circuit representation to identify a fast inverse model that determines the relationship between the geometry parameters of the structure at hand and its operating frequency. Upon suitable correction, the inverse model is applied to find dimensions of the scaled design at the high-fidelity (electromagnetic (EM) simulation) model level. Owing to reasonable correlations between the low- and high-fidelity models, the circuit geometry scaled to a requested operating frequency can be found using just a single EM simulation of the structure, despite possible absolute discrepancies between the models. The proposed methodology is demonstrated using two exemplary compact couplers scaled in wide ranges from 0.5 to 2 GHz and from 0.5 to 1.8 GHz, respectively. The numerical results are supported by physical measurements of the fabricated coupler prototypes. [ABSTRACT FROM PUBLISHER]

This paper introduces a transmission line model analysis for dielectric-loaded self-resonant structures (SRSs). Equivalent circuit models, besides their fast computational time, usually offer some insights into the underlying physics of the electromagnetic structures, hence simplifying and improving the design and optimization criteria of these structures. A systematic approach for deriving impedance expressions of SRS on and within multi-layer dielectric media is presented. The process of extracting equivalent circuit parameters from impedance expressions is explained. The trends of equivalent circuit parameters are studied with different dielectric medium properties and various angles of incidence for an SRS with multiple resonant states highlighting the effect of changing the physical and electrical dimensions on the equivalent circuit parameters. The transmission line model responses are compared to full-wave simulation results, and model accuracy is assessed using the mean absolute error criteria. Furthermore, a new simplified method for calculating the forward transmission coefficient of an SRS within multi-layer media is proposed. This expression offers fundamental insights into the factors contributing to the $S_{21}$ response. Finally, experimental measurements for SRS embedded in a multi-layer dielectric media inside a waveguide is conducted and compared to the equivalent model with very good agreement. [ABSTRACT FROM AUTHOR]

This paper presents a modeling method for high power dielectric resonator (DR) filter. An efficient and accurate model for DR filter is developed by preserving only the necessary information of the generalized scattering matrix (GSM) for each junction of the filter structure. Doing so, the sizes of the GSMs are dramatically reduced for the training and testing of the neural network (NN) models of each critical junction of the DR filter. The neural models are subsequently connected using a transmission line model for each mode in a cylindrical dielectric rod with conductor housing. Calculations of the propagation constant for each mode are given. The final model contains NN and transmission line models and is thus very fast. The method is described in details using a Ku-band high power DR filter. Efficiency and accuracy of the model are demonstrated through comparison with full-wave simulations and measurements. Excellent agreements are observed for both near-band and wide-band results. [ABSTRACT FROM AUTHOR]

This paper presents the design and characterization of millimeter-wave (28/38 GHz), circularly polarized (CP) active antennas, suitable for future 5G services. By augmenting the modeling capabilities of commercially available nonlinear CAD tools, the active antenna design can simultaneously optimize figures of merits for both radiation and nonlinear (NL) performance. The radiating part is computed and optimized layoutwise by means of an artificial neural network (ANN), suitably trained off-line. For the NL design purpose, the harmonic neural network (HNN) of the antenna is subsequently implemented as a standard circuit component, to include the antenna behavior at all the harmonics of its NL regime. This allows avoiding time-consuming electromagnetic (EM)-simulations in the harmonic balance optimization loop. In this way, a well-defined interface between the antenna and the amplifier can be avoided. To demonstrate the effectiveness of the design approach, one active integrated antenna (AIA), consisting of a class AB amplifier feeding a CP patch at 38 GHz, has been fabricated with the standard 0.1- $\mu \text{m}$ AlGaAs-InGaAs pHEMT technology and extensively measured with respect to both the electrical and radiation performances. To reduce fabrication costs, hybrid integration of the antenna and amplifier connection is used, and the antenna is incorporated into the main PCB. [ABSTRACT FROM AUTHOR]

In this paper, the conventional physics-based via (PBV) model is extended to multilayer substrate integrated waveguide (SIW) structures with via feeds. Furthermore, a novel PBV model for a more efficient modeling of multilayer SIW is proposed. This novel method allows to describe SIWs as transmission lines and to model the coupling between the layers with an equivalent circuit near-field model. Both PBV models are compatible and can be combined within a framework for modeling complex multilayer SIWs in an efficient manner. Results obtained by both methods are compared with full-wave solver results and show very good agreement from below cutoff frequency to about three times the cutoff frequency as well as a speed up of several orders of magnitude. [ABSTRACT FROM PUBLISHER]

A new semi-physical memoryless computationally effective behavioral model (BM) capable of predicting amplitude-modulation-to-amplitude-modulation (AM–AM) and amplitude-modulation-to-phase-modulation (AM–PM) distortions is proposed. In recent years, AM–AM and AM–PM distortions have been separately studied in literature. Here, we investigate the correlation between AM–AM and AM–PM distortions first. Based on the observed correlation, a novel AM–PM model is derived from the well-known Rapp AM–AM model. On the basis of the close relationship between the AM–AM and AM–PM model, a highly accurate and computationally effective large-signal BM is obtained. The newly developed model addresses the current needs of the mobile industry that requires BMs of the power amplifier (PA), which can be interpreted by the designers. In addition, the models must be computationally effective due to the limited computation power in mobile handset. Therefore, only memoryless BMs can be taken into account and larger errors are accepted for the sake of computational effectiveness. In this paper, it is shown that the newly introduced model achieves excellent results, when it is applied to actual industrial applications of two GaAs-based class-AB PAs in 65-nm technology and one CMOS-based class AB PA in 28-nm technology, which are designed for mobile communications. [ABSTRACT FROM PUBLISHER]

Metallic compound gratings are studied in this work by means of an analytical equivalent circuit approach in order to obtain its transmission and reflection properties when illuminated by a TM-polarized plane wave. A compound grating consists of the periodic repetition of a finite number of slits carved out of a thick metal slab (reflection grating) or connecting two separated open regions through groups of slits in the metal slab (transmission grating). The equivalent circuit is rigorously obtained starting from a simplified version of the integral equation for the electric field at the slits apertures. That equivalent circuit involves transmission-line sections that account for the fundamental and lowest order diffracted modes (which does give the “dynamical” nature to the present equivalent circuit), and lumped components to model the effect of all the higher order diffracted modes. All the relevant and complex features of the spectra can be satisfactorily explained in terms of the topology and characteristics of the equivalent circuit. In contrast with some previously reported circuit models, all the dynamical and quasi-static circuit elements are analytically and explicitly obtained in terms of the geometric and electrical parameters of the grating. The accuracy of the approximate circuit model is very good over a very wide band, as it is demonstrated by comparison with full-wave data computed with commercial electromagnetic solvers. [ABSTRACT FROM PUBLISHER]

A new method is presented for simulation of nonlinear transmission line circuits based on proper orthogonal decomposition reduction techniques coupled with an efficient interpolatory algorithm. Evaluation of the nonlinear function and corresponding Jacobian is performed in the reduced domain. A key criterion is developed for a priori determination of the dimension of the interpolation space leading to a substantial reduction in the computational cost. The proposed algorithm is applicable to general nonlinear circuits and does not impose any constraints on the topology of the pertinent circuit or type of the nonlinear components. [ABSTRACT FROM AUTHOR]

A new method is introduced for simulation of nonlinear transmission lines based on model-order reduction using the proper-orthogonal decomposition technique. An algorithm is presented for estimating the optimum order of the resulting reduced nonlinear macromodel ensuring its efficiency and accuracy. In contrast to previous publications, the order-estimation algorithm is integrated with the reduction technique leading to significant reduction in the computational cost associated with the generation of the reduced model. [ABSTRACT FROM PUBLISHER]

This work advances the application of neural ordinary differential equations (ODEs) to circuit modeling. Prior works primarily utilized the recurrent neural network (RNN), which is a specific type of neural ODE. In this work, the capability of neural ODEs to represent different types of circuits is studied. Stability conditions are presented, both for neural ODEs in a standalone configuration and for neural ODEs with feedback connections, and practical techniques to impose the stability constraints during training are demonstrated. Based on the theoretical and experimental results, this work provides guidance as to when and how an accurate and stable neural ODE circuit model can be generated. [ABSTRACT FROM AUTHOR]

We present an improved plane-pair partial element equivalent circuit (PEEC) model for power distribution network modeling based on the PEEC formulation. The model can include via connections, decoupling capacitor macro-models, and discontinuities such as holes in the plane-pairs. An efficient approximate inductance sub-meshing model is described for large printed circuit plane-pairs with complex geometries and numerous vias. The modified nodal analysis (MNA) used leads to a flexible circuit solution where we can compute inductances, resistances, impedances, or other circuit models, including dc solutions. The MNA equations include effective optimizations such as the placement of capacitors. Today, a large class of methods are available based on numerous formulations including finite-difference time-domain, finite-element method, integral equation model, and cavity models. Each of the approaches has its own type of problems for which it is most suitable. [ABSTRACT FROM PUBLISHER]

In this article, we propose a new behavioral modeling method to reduce the running complexity and power consumption of digital predistortion (DPD) models for radio frequency power amplifiers. By employing the proposed method, different cross terms in a DPD model can be switched dynamically in real time. Each cross-term branch can further choose the model coefficients from multiple coefficient sets, which improves the DPD performance with little extra complexity. The switch of both cross-term branches and model coefficients is realized using a decision tree-based switch controller, leading to very low run-time complexity. By optimizing the selection process, different input samples can choose the most suitable model configuration that contributes most to the linearization performance. As only one branch is activated at a time, the power consumption can be greatly reduced. Based on the experimental results, the proposed method can achieve excellent linearization performance with significantly reduced power consumption. [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient methodology for the electrothermal characterization of power amplifier (PA) integrated circuits. The proposed electrothermal analysis method predicts the effect of temperature variations on the key performances of PAs, such as gain and linearity, under realistic dynamic operating conditions. A comprehensive technique for identifying an equivalent compact thermal model (CTM), using data from 3-D finite element method thermal simulation and nonlinear curve fitting algorithms, is described. Two efficient methods for electrothermal analysis applying the developed CTM are reported. The validity of the methods is evaluated using commercially available electrothermal computer-aided design (CAD) tools and through extensive pulsed RF signal measurements of a PA device under test. The measurement results confirm the validity of the proposed electrothermal analysis methods. The proposed methods show significantly faster simulation speed compared with available CAD tools for electrothermal analysis. Moreover, the results reveal the importance of electrothermal characterization in the prediction of the temperature-aware PA dynamic operation. [ABSTRACT FROM AUTHOR]

Bit error rate (BER) is an important figure of merit to evaluate the performance of a communication system. Analyzing the BER of a linear-time-invariant system has been extensively studied. However, analyzing the BER of nonlinear circuits and systems is challenging because it cannot rely on linear time invariant (LTI) principles, while an exhaustive nonlinear simulation is computationally prohibitive. To find BER, an exhaustive approach requires nonlinear simulations of $2^{m}$ bit patterns for a channel of $m$ -bit memory, where $m$ can be larger than 100 in today’s high-speed and high-performance design. In this work, we develop a fast and accurate method to analyze the BER of large-scale nonlinear circuits. Only $O(k)$ nonlinear simulations are required, with $k$ much less than $2^{m}$ and independent of $2^{m}$. While performing $O(k)$ nonlinear simulations only, we find a method to determine the probability density function of the nonlinear responses, from which accurate BER results can be obtained. An error assessment method is also developed to evaluate the true error of the nonlinear signaling analysis without the need for knowing the entire nonlinear responses of the channel. Simulations of large-scale real-world nonlinear circuits have demonstrated the accuracy, efficiency, and capacity of the proposed method. A BER as low as 10-56 is accurately predicted. [ABSTRACT FROM AUTHOR]

This article presents a new approach to construct reduced-order models for large nonlinear circuits excited by periodical or quasi-periodical sources. The key feature in the constructed reduced model is that it enables, through solving a much smaller system, tracing the variations in the periodical steady-state response of the original circuit with regards to variations in selected design parameters. The proposed approach is based on the notion of using discrete empirical interpolation to project the nonlinear operator of the circuit equation onto a smaller subspace. In addition, the original system equations are projected onto the subspace spanned by the first few derivatives of the solution with respect to the selected design parameters. Computational savings are made possible through solving a reduced system of equations instead of the full system. Numerical examples are presented to validate the accuracy and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

A passive partial element equivalent circuit (PEEC)-based micromodeling circuit is proposed for time-domain simulation of a high-speed interconnection problem. This physics-based model order reduction method derives a concise and physically meaningful circuit model from the PEEC model by absorbing its insignificant nodes. To maintain high fidelity of the original electromagnetic PEEC model, the concept of pseudoinductor is introduced to the node-absorbing process. The derivation process does not involve any matrix inversion or decomposition and is highly suitable for GPU parallel computations. Passivity of the micromodeling circuit is ensured by a new passivity checking and enforcement method proposed for the first time. As the scale of the micromodeling circuit can be one order of magnitude smaller than that of the original PEEC model, the time-domain simulation can be three orders of magnitude faster. Two practical examples are given to demonstrate the high fidelity, scalability, and accuracy of the proposed micromodeling circuit, showing excellent applicability to high-speed interconnection problems. [ABSTRACT FROM PUBLISHER]

A novel hybrid large-signal model of GaAs pseudomorphic HEMTs (pHEMTs) is proposed for monolithic microwave integrated circuit design. This new model is based upon accurate electromagnetic (EM) description and creative multi-path artificial neural network (ANN) optimization. To precisely describe the EM effect in the high-frequency range, the extrinsic part of this model includes both lumped and distributed components. In order to re-grid the discrete data, the bias-dependent intrinsic elements are determined by ANNs rather than traditional interpolations. The dispersion effect is represented by nonlinear sources with the multi-path-dependent integration technique, which is described by processed multi-bias S-parameters. This proposed approach can be applicable to different bias conditions, which is also verified by different types of GaAs pHEMTs with good agreement. In addition, a class-AB Ka-band power amplifier and a Ka-band switch using a 0.15- \mu \text m GaAs pHEMT process were designed based on the novel hybrid model for further practical verification. [ABSTRACT FROM AUTHOR]

Conventional EM optimization aims to use fewest possible fine model evaluations to increase the speed of optimization. In this work, we propose to use a large number of fine model evaluations to achieve an overall speedup. A large number of fine model evaluations allows us to build a surrogate model valid in a large neighborhood. In the proposed technique, these valid surrogate models are used to achieve large and effective optimization updates, thereby resulting in fewer iterations of the optimization process. Valid surrogate models uses many fine model evaluations which are realized in parallel using hybrid distributed shared memory computing platforms. Parallel computation of large number of fine model evaluations reduces the major computational time required for constructing a surrogate model. Furthermore, we exploit trust region algorithms to guarantee convergence and to re-define the fine model evaluation range in each iteration of the proposed optimization algorithm. The proposed technique aims to increase the speed of gradient based EM optimization when no coarse model (e.g., empirical or equivalent circuits) is available. Three typical examples are used to illustrate the proposed technique. [ABSTRACT FROM AUTHOR]

A new enveloped following (EF) method based on high-order Obreshkov integration formula is presented. The new method is suitable for transient analysis of highly oscillatory circuits with widely separated time-scales. In addition, the proposed algorithm is capable of producing high-order numerically stable approximation of the transient response. The use of high-order formulas leads to a significant reduction in the computational time compared to low-order EF methods because it allows taking large steps in time while keeping the same accuracy. [ABSTRACT FROM AUTHOR]