Designing Photodetectors with Machine Learning

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noma-2022-now5c.2.pdf (283.57 KB)

Links to Files

https://opg.optica.org/abstract.cfm?uri=NOMA-2022-NoW5C.2

Permanent Link

https://doi.org/10.1364/NOMA.2022.NoW5C.2
 http://hdl.handle.net/11603/31810

Collections

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

Author/Creator

Author/Creator ORCID

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

Date

2022-07

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

E. Simsek, I. M. Anjum, T. F. Carruthers, and C. R. Menyuk, "Designing Photodetectors with Machine Learning," in Optica Advanced Photonics Congress 2022, Technical Digest Series (Optica Publishing Group, 2022), paper NoW5C.2. https://doi.org/10.1364/NOMA.2022.NoW5C.2

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Subjects

Abstract

Modern optimization techniques are used to design high-performance photodetectors. All the designs created during these optimization studies are then used to train a physics-inspired, two-stage neural network to obtain even better-performing devices.