Use of Evolutionary Optimization Algorithms for the Design and Analysis of Low Bias, Low Phase Noise Photodetectors

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Use_of_Evolutionary_Optimization_Algorithms_for_the_Design_and_Analysis_of_Low_Bias_Low_Phase_Noise_Photodetectors.pdf (3.14 MB)

Links to Files

https://ieeexplore.ieee.org/abstract/document/10306266

Permanent Link

https://doi.org/10.1109/JLT.2023.3330099
 http://hdl.handle.net/11603/30864

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UMBC Computer Science and Electrical Engineering Department
UMBC Data Science
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0009-0000-3447-6099
 https://orcid.org/0000-0001-9075-7071
 https://orcid.org/0000-0003-4718-6976
 https://orcid.org/0000-0002-5002-1657
 https://orcid.org/0000-0003-0269-8433

Date

2023-11-03

Type of Work

journal articles
Text

Department

Program

Citation of Original Publication

Anjum, Ishraq Md, Ergun Simsek, Seyed Ehsan Jamali Mahabadi, Thomas F. Carruthers, Curtis R. Menyuk, Joe C. Campbell, David A. Tulchinsky, and Keith J. Williams. “Use of Evolutionary Optimization Algorithms for the Design and Analysis of Low Bias, Low Phase Noise Photodetectors.” Journal of Lightwave Technology, 2023, 1–7. https://doi.org/10.1109/JLT.2023.3330099.

Rights

This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0

Subjects

UMBC Computational Photonics for Multilayered Structure (CPMS) Group
UMBC Computational Photonics Laboratory.

Abstract

With the rapid advance of machine learning techniques and the increased availability of high-speed computing resources, it has become possible to exploit machine-learning technologies to aid in the design of photonic devices. In this work we use evolutionary optimization algorithms, machine learning techniques, and the drift-diffusion equations to optimize a modified uni- traveling-carrier (MUTC) photodetector for low phase noise at a relatively low bias of 5 V. We compare the particle swarm optimization (PSO), genetic, and surrogate optimization algorithms. We find that PSO yields the solution with the lowest phase noise, with an improvement over a current design of 4.4 dBc/Hz. We then analyze the machine-optimized design to understand the physics behind the phase noise reduction and show that the optimized design removes electrical bottlenecks in the current design.