Yazi Cao

Slot Antenna for Metal-Rimmed Mobile Handsets

This letter introduces a 15.5 × 56.5-mm2 planar slot antenna operated under surroundings of an unbroken metal rim in mobile handsets. The height of the slot antenna is shortened, and the bandwidth for low band is expanded to cover both GSM850 and GSM900. This antenna provides a solution for planar unbroken metal-rimmed handsets and can be operated in five wireless communication bands (i.e., GSM850, GSM900, DCS1800, PCS1900, and UMTS bands). Good performances on return loss, radiation pattern, efficiency, and gain are obtained over these five operating bands. Hand effect of this slot antenna is also studied by comparison to the conventional monopole antenna.

A Compact Multiband Open-Ended Slot Antenna for Mobile Handsets

A novel compact multiband antenna formed by two printed open-ended slots, with the first one a T-shaped slot and the second one an E-shaped slot, cut at the edge of the ground plane of mobile handsets is presented. This antenna can generate five resonant modes to cover GSM900/DCS1800/ PCS1900/UMTS and 2.4-GHz-based WLAN bands, which can be controlled almost independently by the five respective monopole slots of different lengths. Furthermore, the proposed antenna has a simple planar structure and occupies a small area of only 10 × 42.5 mm2. It is also promising to bend the antenna into a E shape, inverted-T shape and meander line to reduce its volume occupied. Details of the antenna design and experimental results are presented and discussed.

Dynamic Behavioral Modeling of Nonlinear Microwave Devices Using Real-Time Recurrent Neural Network

A novel real-time recurrent neural network (RTRNN) approach is presented for dynamic behavioral macromodeling of nonlinear microwave devices. A modified real-time recurrent learning algorithm is developed to train the neural network model. This proposed RTRNN model can directly be developed from input-output waveform data without having to rely on the internal details of the devices. Once trained, this model provides fast and accurate prediction on the analog behaviors of the nonlinear microwave devices under modeling, which can readily be incorporated into high-level circuit simulation and optimization. This RTRNN approach enhances the neural modeling speed and accuracy. Moreover, it also provides additional flexibility in handing diverse needs of nonlinear microwave circuit designs in the time domain, such as single-tone and multiple-tone simulations and large-signal simulations by comparison to the previously published neural models. The validity of this proposed approach is illustrated through behavioral macromodeling of two typical microwave devices: power amplifiers and pHEMTs.

A New Training Approach for Parametric Modeling of Microwave Passive Components Using Combined Neural Networks and Transfer Functions

This paper presents a novel technique to develop combined neural network and transfer function models for parametric modeling of passive components. In this technique, the neural network is trained to map geometrical variables onto coefficients of transfer functions. A major advance is achieved in resolving the discontinuity problem of numerical solutions of the coefficients with respect to the geometrical variables. Minimum orders of transfer functions for different regions of geometrical parameter space are identified. Our investigations show that varied orders used for different regions result in the discontinuity of coefficients. The gaps between orders are bridged by a new order-changing module, which guarantees the continuity of coefficients and simultaneously maintains the modeling accuracy through a neural network optimization process. This technique is also expanded to include bilinear transfer functions. Once trained, the model provides accurate and fast prediction of the electromagnetic behavior of passive components with geometrical parameters as variables. Compared to conventional training methods, the proposed method allows better accuracy in challenging applications involving high-order transfer functions, wide frequency range, and large geometrical variations. Three examples including parametric modeling of slotted patch antennas, bandstop microstrip filters, and bandpass coupled-line filters are examined to demonstrate the validity of this technique.

A Miniaturized Printed Slot Antenna for Six-Band Operation of Mobile Handsets

A novel miniaturized multiband antenna formed by three folded slots of different lengths cut at the ground plane of the mobile handsets is presented. The proposed antenna can generate six wireless communication bands to cover GSM900/DCS1800/PCS1900/UMTS/2.4-GHz-based WLAN and Satellite DMB bands. Moreover, the antenna has a simple planar structure of small area of only 18×38 mm2. It is also promising to bend the antenna into an L shape or meander line to reduce its volume occupied inside the mobile handsets. Good radiation characteristics, gain, and radiation efficiency are obtained over these six operating bands.

A Wideband and Scalable Model of Spiral Inductors Using Space-Mapping Neural Network

A wideband and scalable model of RF CMOS spiral inductors by virtue of a novel space-mapping neural network (SMNN) is presented. A new modified 2-pi equivalent circuit is used for constructing the SMNN model. This new modeling approach also exploits merits of space-mapping technology. This SMNN model has much enhanced learning and generalization capabilities. In comparison with the conventional neural network and the original 2-pi model, this new SMNN model can map the input-output relationships with fewer hidden neurons and have higher reliability for generalization. As a consequence, this SMNN model can run as fast as an approximate equivalent circuit, yet preserve the accuracy of detailed electromagnetic simulations. Experiments are included to demonstrate merits and efficiency of this new approach.

A Broadband and Parametric Model of Differential Via Holes Using Space-Mapping Neural Network

This letter presents a novel broadband and completely parametric model of differential via holes by virtue of the space-mapping neural network technique. This model consists of a neural network and an equivalent circuit that is utilized to account for various EM effects of differential via holes. The neural network is trained to learn the multi-dimensional mapping between the geometrical variables and the values of independent circuit elements in the equivalent circuit. Once trained with the EM data, this model provides accurate and fast prediction of the EM behavior of differential via holes with geometry parameters as variables. Experiments in comparison with measurement data and EM simulations are included to demonstrate the merits of this new model in both the frequency and time domains.

Parametric Modeling of Microwave Passive Components Using Sensitivity-Analysis-Based Adjoint Neural-Network Technique

This paper presents a novel sensitivity-analysis-based adjoint neural-network (SAANN) technique to develop parametric models of microwave passive components. This technique allows robust parametric model development by learning not only the input–output behavior of the modeling problem, but also derivatives obtained from electromagnetic (EM) sensitivity analysis. A novel derivation is introduced to allow complicated high-order derivatives to be computed by a simple artificial neural-network (ANN) forward-back propagation procedure. New formulations are deduced for exact second-order sensitivity analysis of general multilayer neural-network structures with any numbers of layers and hidden neurons. Compared to our previous work on adjoint neural networks, the proposed SAANN is easier to implement into an existing ANN structure. The proposed technique allows us to obtain accurate and parametric models with less training data. Another benefit of this technique is that the trained model can accurately predict derivatives to geometrical or material parameters, regardless of whether or not these parameters are accommodated as sensitivity variables in EM simulators. Once trained, the SAANN models provide accurate and fast prediction of EM responses and derivatives used for high-level optimization with geometrical or material parameters as design variables. Three examples including parametric modeling of coupled-line filters, cavity filters, and junctions are presented to demonstrate the validity of this technique.

An adjoint dynamic neural network technique for exact sensitivities in nonlinear transient modeling and high-speed interconnect design

We propose a new adjoint dynamic neural network (ADNN) technique aimed at enhancing computer-aided design (CAD) of high-speed VLSI modules. A novel formulation for exact sensitivities is derived employing the Lagrange functions approach, and by defining an adjoint of a dynamic neural network (DNN), for the first time. The proposed ADNN is a dynamic model that we solve using integration backwards through time. One ADNN solution can be used to efficiently compute exact sensitivities of the corresponding DNN with respect to all its parameters. Using these sensitivities, we developed a training algorithm that facilitates DNN learning of nonlinear transients directly from continuous time-domain waveform data. Resulting accurate and fast DNN models can be straightaway used for carrying out high-speed VLSI CAD in SPICE-like time-domain environment. The technique can also speed-up physics-based nonlinear circuit CAD through faster sensitivity computations. Applications of the proposed ADNN technique in transient modeling and nonlinear design are demonstrated through high-speed interconnect driver examples.

Differential Via Modeling Methodology

This paper describes a novel method of modeling the differential via on multilayered printed circuit boards (PCBs) used in high-speed digital designs based on the analytical equations for characteristic impedance and effective dielectric constant. In the absence of measured or electromagnetic simulated data traditionally needed to extract these parameters, this method can quickly and efficiently predict the behavior of the differential via holes on PCBs using a circuit simulator.