Conference Paper

Time-domain neural network characterization for dynamic behavioral models of power amplifiers

Dpt. Ingegneria Elettronica, Univ. Tor Vergata, Rome, Italy
Conference: Gallium Arsenide and Other Semiconductor Application Symposium, 2005. EGAAS 2005. European
Source: IEEE Xplore


This paper presents a black-box model that can be applied to characterize the nonlinear dynamic behavior of power amplifiers. We show that time-delay feed-forward neural networks can be used to make a large-signal input-output time-domain characterization, and to provide an analytical form to predict the amplifier response to multitone excitations. Furthermore, a new technique to immediately extract Volterra series models from the neural network parameters has been described. An experiment based on a power amplifier, characterized with a two-tone power swept stimulus to extract the behavioral model, validated with spectra measurements, is demonstrated.

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Available from: Paolo Colantonio, Oct 10, 2015
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    • "The major advantages of the simulation approach rely on the low cost in time or equipment and the capability to simulate the ICs' performances before realization. Improved description of memory effects based on Volterra series [2] can be found in [3]–[8]. The goal of this study is to efficiently take into account the distortion of the RF envelope signals due to self-heating [9]. "
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    ABSTRACT: System-level models simplify the analysis of complex RF systems, such as transmission-reception modules, by expressing global input-output relationships. However, the development of high RF power models for nonlinear subsystems requires the prediction of the distortion induced by low-frequency memory effects such as self-heating effects. In this framework, we present a new electrothermal behavioral model for power amplifiers. This global model is based on the coupling between a behavioral electrical model derived from the transistor-level description of the amplifier and a thermal reduced model. This model, implemented into a circuit simulator, allows to predict the impact of the thermal effects in pulsed RF mode thanks to an envelope transient analysis. This approach has also been validated by measurements.
    IEEE Transactions on Microwave Theory and Techniques 12/2007; 55(11-55):2290 - 2297. DOI:10.1109/TMTT.2007.907715 · 2.24 Impact Factor
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    • "In this paper a time-domain modeling appoach, using time-delay neural networks (TDNN) to characterize the device dynamic effects adding several time-delayed inputs, is presented. This method has been already successfully applied to build an I/O model of a power amplifier able to predict its nonlinear behaviour in presence of high-order nonlinearity with medium-to-strong memory effects, once trained with input-output time-delayed data samples at different power levels [2]. In this work we present a 2-port model able to learn the nonlinear behavior of a GaN- HEMT device working at a particular frequency (i.e. 1 GHz) and in different classes (A, AB and B). "
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    ABSTRACT: This paper presents a new RF dynamic behavioral model based on a neural network (NN) approach suitable for FET devices in a wide range of working classes, and capable to identify the device response, through the training procedure, for a wide range of input power levels. The presented model has been effectively applied to GaN-based devices at 1 GHz, working in class A and B
    Integrated Nonlinear Microwave and Millimeter-Wave Circuits, 2006 International Workshop on; 03/2006
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    ABSTRACT: The continuing trend of miniaturization in semiconductor devices has enabled the integration of complex functionalities on-chip, leading to a proliferation of wireless devices for both mobile and in-office applications. The use of scaled CMOS technologies for high-frequency wireless devices is posing daunting technological challenges, both in the design and post-manufacture testing of such devices. The issue of device power consumption and heat dissipation is also dominating future wireless transceiver designs. This is driven by the trend of increasing operating speeds coupled with dense integration of multi-mode functionalities onto compact form-factors on-chip. In this thesis, a framework for reliable low-power operation of wireless devices is presented. The presented approaches significantly reduce device test costs during production, and operate the device at very low power consumption levels during field operation of the device. Low-cost test, diagnosis, and tuning techniques to reduce to reduce test cost of devices and operational reliability in field. To reduce device power consumption during field operation, adaptation is performed continuously while ensuring that system-level performance metrics are never violated. This approach has direct implications for boosting the battery life of portable wireless devices while ensuring their operational reliability. Ph.D. Committee Chair: Chatterjee, Abhijit; Committee Member: Anderson, David; Committee Member: Durgin, Gregory; Committee Member: Swaminathan, Madhavan; Committee Member: Zhou, Hao-Min
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