Numerical Optimization of Quantum Cascade Detector Heterostructures

Johannes Popp, Michael Haider, Martin Franckie, Jerome Faist, Christian Jirauschek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We demonstrate a Bayesian optimization framework for quantum cascade (QC) devices in the mid-infrared (mid-IR) and terahertz (THz) regime. The optimization algorithm is based on Gaussian process regression (GPR) and the devices are evaluated using a perturbed rate equation approach based on scattering rates calculated self-consistently by Fermi's golden rule or alternatively extracted from an Ensemble Monte Carlo (EMC) simulation tool. Here, we focus on the optimization of a mid-IR quantum cascade detector (QCD) at a wavelength of 4.7\mu \mathrm{m} with respect to the specific detectivity as a measure for the signal to noise ratio. At a temperature of 220 K we obtain an improvement in specific detectivity by a factor \sim 2.6 to a value of 2.6\times 10{8} Jones.

Original languageEnglish
Title of host publication2020 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2020
PublisherIEEE Computer Society
Pages23-24
Number of pages2
ISBN (Electronic)9781728160863
DOIs
StatePublished - Sep 2020
Event2020 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2020 - Turin, Italy
Duration: 14 Sep 202018 Sep 2020

Publication series

NameProceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
Volume2020-September
ISSN (Print)2158-3234

Conference

Conference2020 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2020
Country/TerritoryItaly
CityTurin
Period14/09/2018/09/20

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