Modeling Of Nonlinear Microwave Devices Research Articles

A Time Delay Neural Network Based Technique for Nonlinear Microwave Device Modeling.

This paper presents a nonlinear microwave device modeling technique that is based on time delay neural network (TDNN). The proposed technique can accurately model the nonlinear microwave devices when compared to static neural network modeling method. A new formulation is developed to allow for the proposed TDNN model to be trained with DC, small-signal, and large signal data, which can enhance the generalization of the device model. An algorithm is formulated to train the proposed TDNN model efficiently. This proposed technique is verified by GaAs metal-semiconductor-field-effect transistor (MESFET), and GaAs high-electron mobility transistor (HEMT) examples. These two examples demonstrate that the proposed TDNN is an efficient and valid approach for modeling various types of nonlinear microwave devices.

A general neuro-space mapping technique for microwave device modeling

Accurate modeling of nonlinear microwave devices is critical for reliable design of microwave circuit and system. In this paper, a more general neuro-space mapping (Neuro-SM) method is proposed to fulfill the needs of the increased modeling complexity. The proposed technique retains the capability of the existing dynamic Neuro-SM in modifying the dynamic voltage relationship between the coarse model and the desired model. The proposed Neuro-SM also considers dynamic current mapping besides voltage mappings. In this way, the proposed Neuro-SM generalizes the previously published Neuro-SM methods and has the potential to produce a more accurate model of microwave devices with more dynamics and nonlinearity. A new formulation and new sensitivity analysis technique are derived to train the general Neuro-SM with dc, small-, and large-signal data. A new gradient-based training algorithm is also proposed to speed up the training. The validity and efficiency of the general Neuro-SM method are demonstrated through a real 2 × 50 μm GaAs pseudomorphic high-electron mobility transistor (pHEMT) modeling example. The proposed general Neuro-SM model can be implemented into circuit simulators conveniently.

A Wiener-Type Dynamic Neural Network Approach to the Modeling of Nonlinear Microwave Devices

A new Wiener-type dynamic neural network (DNN) approach for nonlinear device modeling is proposed in this paper. The proposed analytical formulation of Wiener-type DNN structure consists of a cascade of a simplified linear dynamic part and a nonlinear static part. The simplified linear dynamic equations are obtained by vector fitting enhancing the efficiency of the model. A new formulation and new sensitivity analysis technique is derived to train the proposed Wiener-type DNN with dc, small- and large-signal data. A new gradient-based training algorithm is also formulated to speed up the training of proposed Wiener-type DNN model. The proposed Wiener-type DNN model can be trained to be accurate relative to device data. Furthermore, the proposed Wiener-type DNN provides enhanced convergence properties over existing neural network approaches such as time delay neural network (TDNN) and TDNN with extrapolation. Application examples on modeling GaAs metal-semiconductor-field-effect transistor, GaAs high-electron mobility transistor (HEMT), and a real <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2 \times 50$ </tex-math></inline-formula> gatewidth GaAs pseudomorphic HEMT are presented. The use of Wiener-type DNN model in harmonic balance simulations demonstrates that the Wiener-type DNN is a robust method for developing models for different types of microwave devices. These models could be implemented into circuit simulators conveniently.

A Novel Dynamic Neuro-Space Mapping Approach for Nonlinear Microwave Device Modeling

This letter presents a novel dynamic Neuro-space mapping (Neuro-SM) technique for nonlinear device modeling. This is an advance over the existing static Neuro-SM which aims to map a given approximate device model towards an accurate model. The proposed technique retains the ability of static Neuro-SM in modifying the effects of nonlinear resistors and current sources. The proposed technique can also make up for any capacitive effects and non-quasi-static effects that maybe missing in the given model, which is not achievable by the existing static Neuro-SM. In this way, the dynamic Neuro-SM model can exceed the accuracy limit of the static Neuro-SM. The validity and efficiency of the proposed approach are verified through two high-electron mobility transistor (HEMT) modeling examples.

Compact Behavioral Models of Nonlinear Active Devices Using Response Surface Methodology

This paper presents the response surface methodology in modeling of nonlinear microwave devices. First, different combinations of sampling techniques and types of radial basis functions are evaluated in simulations of the drain current of a 0.15- μm GaAs HEMT transistor described by the Chalmers model. It allows to determine the best settings of the response surface methodology for the modeling of active microwave devices. It is shown that the best sampling strategy is a combination of space-exploration (Voronoi), problem-exploitation (LOLA), and model-error-driven sample rankers. From the various radial basis function models, the fastest convergence is achieved with exponential functions. This knowledge is then used in behavioral modeling of a low-power amplifier AG303 measured in the load-pull setup. It is shown that the response surface methodology outperforms commonly used factorial design of experiments. Moreover, it gives accurate models within just a few tens of samples. However, attention has to be paid at the noisy regions, which might be oversampled by the sampling techniques.

Evolutionary Neuro-Space Mapping Technique for Modeling of Nonlinear Microwave Devices

This paper presents a new advance in Neuro-space mapping (Neuro-SM) techniques for modeling nonlinear microwave devices. Suppose that existing device models (namely, coarse models) cannot match the behavior of a new device (referred to as the fine model). By neural network mapping of the voltage and current signals from the coarse to the fine models, Neuro-SM can modify the behavior of the coarse model to match that of the fine model. However, the efficiency of mapping depends on both the mapping structure and the coarse model. In this paper, a structural optimization technique is presented to achieve optimal combinations of mapping structure and coarse model. An aggressive optimization formulation exploring detailed structural variations in both the mapping and the coarse model is proposed, where the internal branches of coarse models and separate mappings for the voltage and current at gate and drain are used as basic topology variables. The formulation of such a structural optimization by an evolutionary optimization algorithm is proposed. Numerical examples of metal-semiconductor field-effect transistor and high electron-mobility transistor modeling demonstrate that, by using the proposed algorithm, optimal combinations of space mapping and coarse model structures can be achieved leading to the best modeling accuracy with the simplest mapping function.

Statistical Neuro-Space Mapping Technique for Large-Signal Modeling of Nonlinear Devices

A new technique, called statistical neuro-space mapping, is proposed for large-signal statistical modeling of nonlinear microwave devices. The proposed technique is an advance over a recent linear statistical mapping technique. It uses nonlinear mapping to overcome the accuracy limitations of the linear mapping in modeling large statistical variations among different devices. For a given population of device samples, the nominal device model is determined from dc, small-, and large-signal data. The behavior of a random device in the population is obtained by a nonlinear mapping from that of the nominal device. The unknown mapping function is represented by neural networks trained using dc and small-signal data of various devices in the population. A novel statistical mapping is formulated by introducing a compact set of statistical variables to control the mapping to map from the nominal device toward different devices in the population. A new training method is proposed for simultaneous statistical parameter extraction and neural-network training. The proposed technique is confirmed by statistical modeling of microwave transistor examples, and use of the models in statistical analyses of a two-stage amplifier. It is demonstrated that, for small or large statistical variations, the proposed technique outperforms the existing methods, using a minimum amount of expensive large-signal data to provide the most accurate large-signal statistical model.

Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling

A new computer-aided design (CAD) method for automated enhancement of nonlinear device models is presented, advancing the concept of Neuro-space mapping (Neuro-SM). It is a systematic computational method to address the situation where an existing device model cannot fit new device data well. By modifying the current and voltage relationships in the model, Neuro-SM produces a new model exceeding the accuracy limit of the existing model. In this paper, a novel analytical formulation of Neuro-SM is proposed to achieve the same accuracy as the basic formulation of Neuro-SM (known as circuit-based Neuro-SM) with much higher computational efficiency. Through our derivations, the mapping between the existing (coarse) model and the overall Neuro-SM model is analytically achieved for dc, small-signal, and large-signal simulation and sensitivity analysis. The proposed analytical formulation is a significant advance over the circuit-based Neuro-SM, due to the elimination of extra circuit equations needed in the circuit-based formulation. A two-phase training algorithm utilizing gradient optimization is also developed for fast training of the analytical Neuro-SM models. Application examples on modeling heterojunction bipolar transistor (HBT), metal-semiconductor-field-effect transistor (MESFET), and high-electron mobility transmistor (HEMT) devices and the use of Neuro-SM models in harmonic balance simulations demonstrate that the analytical Neuro-SM is an efficient approach for modeling various types of microwave devices. It is useful for systematic and automated update of nonlinear device model library for existing circuit simulators.