B. Chattaraj

Carleton University, Ottawa, Ontario, Canada

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Publications (5)4.46 Total impact

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    ABSTRACT: In this paper, artificial neural-network approaches to electromagnetic (EM)-based modeling in both frequency and time domains and their applications to nonlinear circuit optimization are presented. Through accurate and fast EM-based neural models of passive components, we enable consideration of EM effects in high-frequency and high-speed computer-aided design, including component's geometrical/physical parameters as optimization variables. Formulations for standard frequency-domain neural modeling approach, and recent time-domain neural modeling approach based on state--space concept, are described. A new EM-based time-domain neural modeling approach combining existing knowledge in the form of equivalent circuits (ECs), with state--space equations (SSEs) and neural networks (NNs), called the EC--SSE--NN, is proposed. The EC--SSE--NN models allow EM behaviors of passive components in the circuit to interact with nonlinear behaviors of active devices, and facilitate nonlinear circuit optimization in the time domain. An automatic mechanism for EM data generation, which can lead to efficient training of neural models for EM components, is presented. Demonstration examples including EM-based frequency-domain optimization of a three-stage amplifier, time-domain circuit optimization in a multilayer printed circuit board, including geometrical/physical -oriented neural models of power-plane effects, and EM-based optimization of a high-speed interconnect circuit with embedded passive terminations and nonlinear buffers in the time domain are presented.
    04/2004;
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    [show abstract] [hide abstract]
    ABSTRACT: In this paper, artificial neural-network approaches to electromagnetic (EM)-based modeling in both frequency and time domains and their applications to nonlinear circuit optimization are presented. Through accurate and fast EM-based neural models of passive components, we enable consideration of EM effects in high-frequency and high-speed computer-aided design, including component's geometrical/physical parameters as optimization variables. Formulations for standard frequency-domain neural modeling approach, and recent time-domain neural modeling approach based on state-space concept, are described. A new EM-based time-domain neural modeling approach combining existing knowledge in the form of equivalent circuits (ECs), with state-space equations (SSEs) and neural networks (NNs), called the EC-SSE-NN, is proposed. The EC-SSE-NN models allow EM behaviors of passive components in the circuit to interact with nonlinear behaviors of active devices, and facilitate nonlinear circuit optimization in the time domain. An automatic mechanism for EM data generation, which can lead to efficient training of neural models for EM components, is presented. Demonstration examples including EM-based frequency-domain optimization of a three-stage amplifier, time-domain circuit optimization in a multilayer printed circuit board, including geometrical/physical-oriented neural models of power-plane effects, and EM-based optimization of a high-speed interconnect circuit with embedded passive terminations and nonlinear buffers in the time domain are presented.
    IEEE Transactions on Microwave Theory and Techniques 02/2004; · 2.23 Impact Factor
  • [show abstract] [hide abstract]
    ABSTRACT: In this paper, we propose an efficient knowledge-based automatic model generation (KAMG) technique aimed at generating microwave neural models of the highest possible accuracy using the fewest accurate data. The technique is comprehensively derived to integrate three distinct powerful concepts, namely, automatic model generation, knowledge neural networks, and space mapping. For the first time, we simultaneously utilize two types of data generators, namely, coarse data generators that are approximate and fast (e.g., two-and-one-half-dimensional electromagnetic), and fine data generators that are accurate and slow (e.g., three-dimensional electromagnetic). Motivated by the space-mapping concept, the KAMG technique utilizes extensive coarse data, but fewest fine data to generate neural models that accurately match the fine data. Our formulation exploits a variety of knowledge neural-network architectures to facilitate reinforced neural-network learning from coarse and fine data. During neural model generation by KAMG, both coarse and fine data generators are automatically driven using adaptive sampling. The KAMG technique helps to increase the efficiency of neural model development by taking advantage of a microwave reality, i.e., availability of multiple sources of training data for most high-frequency components. The advantages of the proposed KAMG technique are demonstrated through practical microwave examples of MOSFET and embedded passive components used in multilayer printed circuit boards.
    IEEE Transactions on Microwave Theory and Techniques 08/2003; · 2.23 Impact Factor
  • [show abstract] [hide abstract]
    ABSTRACT: In this paper, we propose an efficient Knowledge based Automatic Model Generation (KAMG) technique, aimed at generating microwave neural models of highest possible accuracy using fewest accurate data. The technique is comprehensively derived to integrate three distinct powerful concepts, namely, automatic model generation, knowledge neural networks and space mapping. We utilize two types of data generators - fine data generators that are accurate and slow (e.g., CPU-intensive 3D-EM simulators); coarse data generators that are approximate and fast (e.g., inexpensive 2D-EM). Motivated by the space-mapping concept, the KAMG utilizes extensive approximate data but fewest accurate data to generate neural models that accurately match fine data. Our formulation exploits a variety of knowledge network architectures to facilitate reinforced neural network learning from both coarse and fine data. During neural model generation by KAMG both coarse and fine data generators are automatically driven using adaptive sampling. The proposed technique is demonstrated through examples of MOSFET, and embedded passives used in multi-layer PCBs
    Microwave Symposium Digest, 2002 IEEE MTT-S International; 02/2002
  • B. Chattaraj, M.C.E. Yagoub, X. Ding, Q.J. Zhang
    [show abstract] [hide abstract]
    ABSTRACT: This paper describes a new formulation of the full EM representation and optimization of passive devices based on neural networks. The proposed models allow dynamic geometric parameter based design and are applied to circuit optimization. To illustrate our method, a decoupling capacitor and a power plane used in an amplifier example, are placed efficiently with respect to the voltage source and other components connected to it using EM based optimization to reduce the maximum amount of power source noise.
    Microwave Conference, 2001. 31st European; 10/2001