Alina Caddemi

Bio: Alina Caddemi is an academic researcher from University of Messina. The author has contributed to research in topics: Equivalent circuit & High-electron-mobility transistor. The author has an hindex of 26, co-authored 166 publications receiving 2144 citations. Previous affiliations of Alina Caddemi include United States Naval Research Laboratory.

Accurate Multibias Equivalent-Circuit Extraction for GaN HEMTs

Abstract: This paper focuses on the determination and analysis of an accurate small-signal equivalent circuit for gallium-nitride high electron-mobility transistors under different bias conditions. Our experimental results show that a channel capacitance has to be added to the conventional forward "cold" model for modeling the device-under-test. The validity of the proposed extraction procedure has been verified by the very good agreement between simulated and measured scattering parameters up to 50 GHz

Nonlinear Dispersive Modeling of Electron Devices Oriented to GaN Power Amplifier Design

Abstract: This paper presents a new modeling approach accounting for the nonlinear description of low-frequency dispersive effects (due to thermal phenomena and traps) affecting electron devices. The theoretical formulation is quite general and includes as particular cases different models proposed in the literature. A large set of experimental results, oriented to microwave GaN power amplifier design, is provided to give an exhaustive validation under realistic device operation.

A New Millimeter-Wave Small-Signal Modeling Approach for pHEMTs Accounting for the Output Conductance Time Delay

Abstract: A new technique is developed for determining analytically a millimeter-wave small-signal equivalent-circuit model of GaAs pseudomorphic HEMTs from scattering parameter measurements. In order to obtain a good agreement between model simulations and measurements up to 90 GHz, the conventional intrinsic output conductance is substituted by a voltage-controlled current source with a time delay. Consequently, a simple and accurate extraction procedure is proposed for taking into account the introduction of the output conductance time delay.

Neural approach for temperature-dependent modeling of GaN HEMTs

Abstract: Gallium nitride high electron-mobility transistors have gained much interest for high-power and high-temperature applications at high frequencies Therefore, there is a need to have the dependence on the temperature included in their models To meet this challenge, the present study presents a neural approach for extracting a multi-bias model of a gallium nitride high electron-mobility transistors including the dependence on the ambient temperature Accuracy of the developed model is verified by comparing modeling results with measurements Copyright © 2014 John Wiley & Sons, Ltd

A comprehensive review on microwave FinFET modeling for progressing beyond the state of art

Abstract: FinFET is a multiple-gate silicon transistor structure that nowadays is attracting an extensive attention to progress further into the nanometer era by going beyond the downscaling limit of the conventional planar CMOS technology. Although the interest for this architecture has been mainly devoted to digital applications, the analysis at high frequency is crucial for targeting a successful mixed integration of analog and digital circuits. In view of that, the purpose of this review paper is to provide a clear and exhaustive understanding of the state of art, challenges, and future trends of the FinFET technology from a microwave modeling perspective. Inspired by the traditional modeling techniques for conventional MOSFETs, different strategies have been proposed over the last years to model the FinFET behavior at high frequencies. With the aim to support the development of this technology, a comparative study of the achieved results is carried out to gain both a useful feedback to investigate the microwave FinFET performance as well as a valuable modeling know-how. To accomplish a comprehensive review, all aspects of microwave modeling going from linear (also noise) to non-linear high-frequency models are addressed.

IEEE Transactions on Microwave Theory and Techniques and Antennas and Propagation Announce a Joint Special Issue on Ultra-Wideband (UWB) Technology

Abstract: Before the emergence of ultra-wideband (UWB) radios, widely used wireless communications were based on sinusoidal carriers, and impulse technologies were employed only in specific applications (e.g. radar). In 2002, the Federal Communication Commission (FCC) allowed unlicensed operation between 3.1–10.6 GHz for UWB communication, using a wideband signal format with a low EIRP level (−41.3dBm/MHz). UWB communication systems then emerged as an alternative to narrowband systems and significant effort in this area has been invested at the regulatory, commercial, and research levels.

Behavioral Modeling and Linearization of Wideband RF Power Amplifiers Using BiLSTM Networks for 5G Wireless Systems

Abstract: Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non causality concern. Next, an additional model is proposed in this paper to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability.

Accurate Multibias Equivalent-Circuit Extraction for GaN HEMTs

Abstract: This paper focuses on the determination and analysis of an accurate small-signal equivalent circuit for gallium-nitride high electron-mobility transistors under different bias conditions. Our experimental results show that a channel capacitance has to be added to the conventional forward "cold" model for modeling the device-under-test. The validity of the proposed extraction procedure has been verified by the very good agreement between simulated and measured scattering parameters up to 50 GHz

An Improved Small-Signal Parameter-Extraction Algorithm for GaN HEMT Devices

Abstract: A highly efficient and accurate extraction algorithm for the small-signal equivalent-circuit parameters of a GaN high electron-mobility transistor device is presented. Elements of the extrinsic equivalent-circuit topology are evaluated using a modified "cold field-effect transistor" approach whereby the undesirable need to forward bias the device's gate terminal is avoided. Intrinsic elements are determined based on a circuit topology, which identifies, for the first time, a time delay in the output conductance of GaN-based devices. The validity of the proposed algorithm has been thoroughly verified with excellent correlation between the measured and modeled S-parameters up to 50 GHz.

Nonlinear Dispersive Modeling of Electron Devices Oriented to GaN Power Amplifier Design

Abstract: This paper presents a new modeling approach accounting for the nonlinear description of low-frequency dispersive effects (due to thermal phenomena and traps) affecting electron devices. The theoretical formulation is quite general and includes as particular cases different models proposed in the literature. A large set of experimental results, oriented to microwave GaN power amplifier design, is provided to give an exhaustive validation under realistic device operation.