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Mohamad EL CHAAR

Adaptive Distributed Amplifier using Artificial Intelligence

Distributed Amplifier, Wide band, Adaptive, Mmic, Millimeter-waves, Bist

The objective of this PhD thesis is to address the issues related to amplification for high data-rate applications such as multi-gigabit wireless and wireline communication links. Requirements for high data-rates, above 100-Gb/s, concern the need for operating bandwidths above 50 GHz and, of course, maintining low power consumption. Distributed amplifiers (DA) are promising candidates for these kind of systems due to their superior operation bandwidth over other broadband topologies. Working in millimeter region, however, poses challenges. Challenges in terms of designing, determining the DA parameters needed to attain the optimum performance a given technology can offer, in terms of layout, especially when dealing with active cells and their interconnections, in terms of challenges when trying to maintain high gain for small power millimeter-wave signals, and also challenges due to process variation. For those reasons, as a first step, this PhD work will consist to model, design and layout distributed amplifiers of extremely large bandwidths and high gain in silicon technology then, as a second step, to implement built-in techniques for self-test (BIST), self-calibration, self-healing and adaptive operation. Performing design should pave the way towards state-of-the-art results.


Thesis director: Florence PODEVIN
Thesis supervisor: Sylvain BOURDEL - Manuel BARRAGAN
Thesis started on: Oct. 2019
Thesis defence: 29/09/2022
Doctoral school: EEATS

Submitted on January 12, 2022

Updated on July 28, 2022