unknown

Analytic and Machine Learning Based Design of Monolithic Transistor-Antenna for Plasmonic Millimeter-Wave Detectors

Abstract

Department of Electrical EngineeringThis thesis reports an advanced analysis on a monolithic transistor-antenna by designing a ring-type asymmetric FET itself as a receiving antenna element which receives millimeter-waves in a loss-less manner with a plasmonic ampli fication for millimeter-wave (mmW) detectors. The proposed transistor-antenna device combines the plasmonic and the electromagnetic (EM) aspects in a single place. As a result, it can absorb the incoming mmW and transfer power directly to the ring-type asymmetric channel without any feeding line and a separate antenna element. Both the charge asymmetry in the device channel and the antenna coupling are contributing to the enhanced photoresponse. Among the two factors, the improved antenna coupling is more dominant in the performance enhancement of our proposed design. Also, our transistor-antenna device have enhanced performance with a uniformly enhanced responsivity of every pixel by characterizing its impedance exactly pursuing real-time mmW imaging. Operation principle of the proposed device is discussed, focusing on how signal transmission through the ring-type structure is available without any feeding line between the antenna and the detector. To determine the antenna geometry aiming for a desired resonant frequency, we present an efficient design procedure based on periodic bandgap analysis combined with parametric electromagnetic simulations. From a fabricated ring-type FET-based monolithic antenna device, we demonstrated the highly enhanced optical responsivity and the reduced optical noise-equivalent power, which are in comparable order with the reported state-of-the-art CMOS-based antenna integrated direct detectors. Another part of the thesis focuses on developing machine learning models to enable fast, accurate design and veri fication of electromagnetic structures. We proposed a novel Bayesian learning algorithm named as Bayesian clique learning, for searching the optimal electromagnetic design parameter by using the structural property of EM simulation data set. Along with this, we also given an inverse problem approach for designing the electromagnetic structures which suggest going in the opposite direction to determine the design parameters from characteristics of the desired output.clos

    Similar works