The Experts below are selected from a list of 56511 Experts worldwide ranked by ideXlab platform

Qi-jun Zhang - One of the best experts on this subject based on the ideXlab platform.

  • a novel dynamic neuro space mapping approach for nonlinear microwave Device Modeling
    IEEE Microwave and Wireless Components Letters, 2016
    Co-Authors: Lin Zhu, Qi-jun Zhang, Kaihua Liu, Bo Peng, Shuxia Yan
    Abstract:

    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.

  • An overview of Neuro-space mapping techniques for microwave Device Modeling
    2016 IEEE MTT-S Latin America Microwave Conference (LAMC), 2016
    Co-Authors: Wenyuan Liu, Lin Zhu, Qi-jun Zhang
    Abstract:

    This paper presents an overview of Neuro-space mapping (Neuro-SM) approach and its application to nonlinear Device Modeling. The Neuro-SM approach addresses the situation where an existing Device model cannot fit new Device data well. By modifying the current and voltage relationships in the model, the Neuro-SM produces a new model exceeding the accuracy limit. This paper describes several Neuro-SM techniques incorporating static Neuro-SM, advanced static Neuro-SM, and dynamic Neuro-SM techniques for microwave Device Modeling. A real 2 × 50 gatewidths GaAs pseudomorphic high-electron mobility transistor (pHEMT) Modeling example is used to illustrate the accuracy and efficiency of dynamic Neuro-SM.

  • Neuro-space mapping technique for semiconductor Device Modeling
    Optimization and Engineering, 2007
    Co-Authors: Lei Zhang, Qi-jun Zhang
    Abstract:

    This paper presents an application of the space mapping concept in the Modeling of semiconductor Devices. A recently proposed Device Modeling technique, called neuro-space mapping (Neuro-SM), is described to meet the constant need of new Device models due to rapid progress in the semiconductor technology. Neuro-SM is a systematic method allowing us to exceed the present capabilities of the existing Device models. It uses a neural network to map the voltage and current signals between an existing Device model (coarse model) and the actual Device behavior (fine model), such that the mapped model becomes an accurate representation of the new Device. An efficient training method based on analytical sensitivity analysis for such mapping neural network is also addressed. The trained Neuro-SM model can retain the speed of the existing Device model while improving the model accuracy. The benefit of the Neuro-SM method is demonstrated by examples of SiGe HBT and GaAs MESFET Modeling and use of the models in harmonic balance simulation.

  • Neuro-Space Mapping technique for nonlinear Device Modeling and large signal simulation
    IEEE MTT-S International Microwave Symposium Digest 2003, 2003
    Co-Authors: Lei Zhang, Runtao Ding, Jianjun Xu, Mustapha C. E. Yagoub, Qi-jun Zhang
    Abstract:

    A new Neuro-Space Mapping (Neuro-SM) approach is presented enabling the space mapping (SM) concept to be applied to nonlinear Device Modeling and large signal circuit simulation. Suppose that an existing Device model (namely, the coarse model) cannot match the actual Device behavior (namely, the fine model). Using the proposed technique, the voltage and current signals between the coarse and the fine Device models are mapped by a neural network. This mapping automatically modifies the behavior of the coarse model such that the mapped model accurately matches the actual Device behavior. New training methods for such mapping neural networks are proposed. Examples of SiGe HBT and GaAs MESFET Modeling and use of the models in harmonic balance simulation demonstrate that Neuro-SM is a systematic method to allow us to exceed the present capabilities of the existing Device models.

  • a generalized space mapping tableau approach to Device Modeling
    IEEE Transactions on Microwave Theory and Techniques, 2001
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, Jose E Rayassanchez, Qi-jun Zhang
    Abstract:

    A comprehensive framework to engineering Device Modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave Devices can be significantly enhanced. We present three fundamental illustrations: a basic space-mapping super model (SMSM), frequency-space-mapping super model (FSMSM) and multiple space mapping (MSM). Two variations of MSM are presented: MSM for Device responses and MSM for frequency intervals. We also present novel criteria to discriminate between coarse models of the same Device. The SMSM, FSMSM, and MSM concepts have been verified on several Modeling problems, typically utilizing a few relevant full-wave electromagnetic simulations. This paper presents four examples: a microstrip line, a microstrip right-angle bend, a microstrip step junction, and a microstrip shaped T-junction, yielding remarkable improvement within regions of interest.

Vladimir Bulovic - One of the best experts on this subject based on the ideXlab platform.

  • organic electronic Device Modeling at the nanoscale
    International Conference on Computer Aided Design, 2006
    Co-Authors: Conor F Madigan, Vladimir Bulovic
    Abstract:

    Electronic Devices with nanoscale features (~ 100 nm or smaller) are becoming increasingly important in electronics technology. While nanoscale electronic Devices comprise a variety of different material sets and structures, many of the nanoscale Devices developed in the last decade employ organic materials. In this talk, we discuss the Modeling of organic electronic thin film Devices. In our approach, analysis of such Devices begins on the molecular scale, and Device level behavior is then derived from the combination of individual molecular properties and physical models of intermolecular interactions. We present a general purpose Monte Carlo simulator based on molecular-scale physical models and employ this simulator to analyze Device behavior.

  • ICCAD - Organic electronic Device Modeling at the nanoscale
    Proceedings of the 2006 IEEE ACM international conference on Computer-aided design - ICCAD '06, 2006
    Co-Authors: Conor F Madigan, Vladimir Bulovic
    Abstract:

    Electronic Devices with nanoscale features (~ 100 nm or smaller) are becoming increasingly important in electronics technology. While nanoscale electronic Devices comprise a variety of different material sets and structures, many of the nanoscale Devices developed in the last decade employ organic materials. In this talk, we discuss the Modeling of organic electronic thin film Devices. In our approach, analysis of such Devices begins on the molecular scale, and Device level behavior is then derived from the combination of individual molecular properties and physical models of intermolecular interactions. We present a general purpose Monte Carlo simulator based on molecular-scale physical models and employ this simulator to analyze Device behavior.

J.w. Bandler - One of the best experts on this subject based on the ideXlab platform.

  • a generalized space mapping tableau approach to Device Modeling
    IEEE Transactions on Microwave Theory and Techniques, 2001
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, Jose E Rayassanchez, Qi-jun Zhang
    Abstract:

    A comprehensive framework to engineering Device Modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave Devices can be significantly enhanced. We present three fundamental illustrations: a basic space-mapping super model (SMSM), frequency-space-mapping super model (FSMSM) and multiple space mapping (MSM). Two variations of MSM are presented: MSM for Device responses and MSM for frequency intervals. We also present novel criteria to discriminate between coarse models of the same Device. The SMSM, FSMSM, and MSM concepts have been verified on several Modeling problems, typically utilizing a few relevant full-wave electromagnetic simulations. This paper presents four examples: a microstrip line, a microstrip right-angle bend, a microstrip step junction, and a microstrip shaped T-junction, yielding remarkable improvement within regions of interest.

  • A Generalized Space Mapping Tableau Approach to Device Modeling
    1999 29th European Microwave Conference, 1999
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, J.e. Rayas-sanchez, Q.j. Zhang
    Abstract:

    A novel, comprehensive framework to engineering Device Modeling called Generalized Space Mapping (GSM) is introduced. The accuracy of available empirical models of microwave Devices can be significantly enhanced by exploiting GSM. We present three fundamental illustrations: a basic Space Mapping Super Model (SMSM), a basic Frequency-Space Mapping Super Model (FSMSM) and Multiple Space Mapping (MSM). The new concept is verified on several Device Modeling problems, typically utilizing very few full-wave EM simulations, yielding remarkable improvement in accuracy.

M.a. Ismail - One of the best experts on this subject based on the ideXlab platform.

  • a generalized space mapping tableau approach to Device Modeling
    IEEE Transactions on Microwave Theory and Techniques, 2001
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, Jose E Rayassanchez, Qi-jun Zhang
    Abstract:

    A comprehensive framework to engineering Device Modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave Devices can be significantly enhanced. We present three fundamental illustrations: a basic space-mapping super model (SMSM), frequency-space-mapping super model (FSMSM) and multiple space mapping (MSM). Two variations of MSM are presented: MSM for Device responses and MSM for frequency intervals. We also present novel criteria to discriminate between coarse models of the same Device. The SMSM, FSMSM, and MSM concepts have been verified on several Modeling problems, typically utilizing a few relevant full-wave electromagnetic simulations. This paper presents four examples: a microstrip line, a microstrip right-angle bend, a microstrip step junction, and a microstrip shaped T-junction, yielding remarkable improvement within regions of interest.

  • A Generalized Space Mapping Tableau Approach to Device Modeling
    1999 29th European Microwave Conference, 1999
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, J.e. Rayas-sanchez, Q.j. Zhang
    Abstract:

    A novel, comprehensive framework to engineering Device Modeling called Generalized Space Mapping (GSM) is introduced. The accuracy of available empirical models of microwave Devices can be significantly enhanced by exploiting GSM. We present three fundamental illustrations: a basic Space Mapping Super Model (SMSM), a basic Frequency-Space Mapping Super Model (FSMSM) and Multiple Space Mapping (MSM). The new concept is verified on several Device Modeling problems, typically utilizing very few full-wave EM simulations, yielding remarkable improvement in accuracy.

N. Georgieva - One of the best experts on this subject based on the ideXlab platform.

  • a generalized space mapping tableau approach to Device Modeling
    IEEE Transactions on Microwave Theory and Techniques, 2001
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, Jose E Rayassanchez, Qi-jun Zhang
    Abstract:

    A comprehensive framework to engineering Device Modeling, which we call generalized space mapping (GSM) is introduced in this paper. GSM permits many different practical implementations. As a result, the accuracy of available empirical models of microwave Devices can be significantly enhanced. We present three fundamental illustrations: a basic space-mapping super model (SMSM), frequency-space-mapping super model (FSMSM) and multiple space mapping (MSM). Two variations of MSM are presented: MSM for Device responses and MSM for frequency intervals. We also present novel criteria to discriminate between coarse models of the same Device. The SMSM, FSMSM, and MSM concepts have been verified on several Modeling problems, typically utilizing a few relevant full-wave electromagnetic simulations. This paper presents four examples: a microstrip line, a microstrip right-angle bend, a microstrip step junction, and a microstrip shaped T-junction, yielding remarkable improvement within regions of interest.

  • A Generalized Space Mapping Tableau Approach to Device Modeling
    1999 29th European Microwave Conference, 1999
    Co-Authors: J.w. Bandler, N. Georgieva, M.a. Ismail, J.e. Rayas-sanchez, Q.j. Zhang
    Abstract:

    A novel, comprehensive framework to engineering Device Modeling called Generalized Space Mapping (GSM) is introduced. The accuracy of available empirical models of microwave Devices can be significantly enhanced by exploiting GSM. We present three fundamental illustrations: a basic Space Mapping Super Model (SMSM), a basic Frequency-Space Mapping Super Model (FSMSM) and Multiple Space Mapping (MSM). The new concept is verified on several Device Modeling problems, typically utilizing very few full-wave EM simulations, yielding remarkable improvement in accuracy.