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Device Modeling Insights From The Keysight Experts

Who are the Experts in Device Modeling and Characterization?

Keysight Technologies has a number of experts in the field of device modeling and characterization.

David E. Root

Dr. David E. RootKeysight Research Fellow Dr. David E. Root is widely recognized across industry and academia as a leading expert in modeling and measurement science, known for his innovative interdisciplinary contributions that have changed the field. He holds a Ph.D. in physics from MIT and has published over 100 technical articles. His current responsibilities include nonlinear behavioral and device modeling, large-signal simulation, and nonlinear measurements for new technical capabilities and business opportunities for Keysight. He is a fellow of the IEEE.

Here are several papers that Dr. Root has written and/or collaborated on related to the subject of Device Modeling:

  • D. E. Root, “Future Device Modeling Trends,” IEEE Microwave Magazine, Nov./Dec. 2012, pp 45-59
    An extend survey of modern measurement-based nonlinear transistor modeling methods. Three very different flows are based on large-signal data from recent nonlinear vector network analyzer (NVNA) instruments. Artificial neural network (ANN) techniques are key elements in two of the flows. X-parameters applied to the transistor are highlighted in one approach.
  • Roblin, P.; Root, D.E.; Verspecht, J.; Ko, Y.; Teyssier, J.P. , “New Trends for the Nonlinear Measurement and Modeling of High-Power RF Transistors and Amplifiers With Memory Effects,” IEEE Transactions Microwave Theory and Techniques, Volume: 60 , Issue: 6 , part 2: pp. 1964 – 1978, 2012
    New trends for the characterization, device modeling, and behavioral modeling of power transistors and amplifiers with strong memory effects are presented. LSNA/NVNA techniques, trapping and electro-thermal models, and dynamic long-term memory extensions to X-parameters are presented.
  • D. E. Root, J. Verspecht, D. Sharrit, J. Wood, and A. Cognata, “Broad-Band, Poly-Harmonic Distortion (PHD) Behavioral Models from Fast Automated Simulations and Large-Signal Vectorial Network Measurements,” IEEE Transactions on Microwave Theory and Techniques Vol. 53. No. 11, November, 2005 pp. 3656-3664
    Early and often-cited paper on the foundations of what would later be called X-parameters. “Offset-frequency” method, based on mixer theory, used to derive the X-parameter equations (in an early notation). Simulation-derived and measurement-based X-parameter models presented and validated.
  • J. Wood, D. E. Root, N. B. Tufillaro, “A behavioral modeling approach to nonlinear model-order reduction for RF/microwave ICs and systems,” IEEE Transactions on Microwave Theory and Techniques, Vol. 52, Issue 9, Part 2, Sept. 2004 pp. 2274-2284
    This paper considers an approach to nonlinear model-order reduction for RF/microwave integrated circuits (ICs) from the perspective of “black-box” time-domain behavioral modeling. A systematic methodology for creating behavioral models using nonlinear system identification techniques, nonlinear dynamics, computational geometry, and information theory is presented.
  • Jianjun Xu; Daniel Gunyan, Masaya Iwamoto, Alex Cognata, and David E. Root, “Measurement-Based Non-Quasi-Static Large-Signal FET Model Using Artificial Neural Networks, ” IEEE International Microwave Symposium Digest, 2006, pp 469-472.
    Introduces the foundations for the Keysight NeuroFET model, a non-quasi-static nonlinear FET model using artificial neural networks for the model current and charge constitutive relations. Model functions are trained using conventional and recently invented adjoint methods, from automatically characterized device DC and S-parameter data.
  • Jianjun Xu, Daniel Gunyan, Masaya Iwamoto, Jason M. Horn, Alex Cognata, and David E. Root , “Drain-Source Symmetric Artificial Neural Network-Based FET Model with Robust Extrapolation BeyondTraining Data,” IEEE International Microwave Symposium Digest, 2007; pp 2011-2014.
    Extensions of NeuroFET model to include the exact mathematical symmetry of drain-source interchange. Makes NeuroFET methodology applicable to devices designed for mixers where Vds(t) crosses zero. Also includes extrapolation algorithm for robust convergence beyond training data.
  • Jianjun Xu, Jason Hom, Masaya Iwamoto, and David E. Root, “Large-signal FET Model with Multiple Time Scale Dynamics from Nonlinear Vector Network Analyzer Data,” IEEE International Microwave Symposium Digest, 2010; pp 417-420
    Introduces the Keysight DynaFET model, an advanced compact model of III-V FETs (GaAs and GaN) including electro-thermal and trapping effects of gate-lag and drain-lag. The model constitutive relations are computed using artificial neural network training methods applied to thousands of nonlinear waveform measurements taken by the Keysight NVNA instrument.

Learn more about Keysight's Device Modeling and Characterization solutions.

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