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Bayesian Optimization With Improved Scalability and Derivative Information for Efficient Design of Nanophotonic Structures

We propose the combination of forward shape derivatives and the use of an iterative inversion scheme for Bayesian optimization to find optimal designs of nanophotonic devices. This approach widens the range of applicability of Bayesian optmization to situations where a larger number of iterations is required and where derivative information is available. This was previously impractical because the computational efforts required to identify the next evaluation point in the parameter space became much larger than the actual evaluation of the objective function. We demonstrate an implementation of the method by optimizing a waveguide edge coupler.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.1 January 1 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 167 - 177

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MACHINE LEARNING
SILICON PHOTONICS
BAYESIAN OPTIMIZATION
PHOTONIC DESIGN

Info Detail Spesifik

DOI: 10.1109/JLT.2020.3023450

Pernyataan Tanggungjawab

Xavier Garcia-Santiago, Sven Burger, Carsten Rockstuhl, Philipp-Immanuel Schneider

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