Guaranteed global optimization of thin-film optical systems
Name
Azunre_2019_New_J._Phys._21_073050.pdf
Description
Published version
Size
2.34 MB
Format
Adobe PDF
Checksum (MD5)
e63210c3225de6e2d29bf9af772a6e36
Author(s)
Azunre, Paul
Jean, Joel
Rotschild, Carmel
Bulovic, Vladimir
Johnson, Steven G
Baldo, Marc A
Date Issued
2019
Journal
New Journal of Physics
Publisher
IOP Publishing
Version
Final published version
Abstract
© 2019 The Author(s). Published by IOP Publishing Ltd on behalf of the Institute of Physics and Deutsche Physikalische Gesellschaft. A parallel deterministic global optimization algorithm for thin-film multilayer optical coatings is developed. This algorithm enables locating a global solution to an optimization problem in this class to within a user-specified tolerance. More specifically, the algorithm is a parallel branch-And-bound method with applicable bounds on the merit function computed using Taylor models. This study is the first one, to the best of our knowledge, to attempt guaranteed global optimization of this important class of problems, thereby providing an overview and an assessment of the current state of such techniques in this domain. As a proof of concept on a small scale, the method is illustrated numerically and experimentally in the context of antireflection coatings for silicon solar cells-we design and fabricate a three-layer dielectric stack on silicon that exhibits an average reflectance of (2.53 0.10)%, weighted over a broad range of incident angles and the solar spectrum. The practicality of our approach is assessed by comparing its computational cost relative to traditional stochastic global optimization techniques which provide no guarantees on their solutions. While our method is observed to be significantly more computationally expensive, we demonstrate via our proof of concept that it is already feasible to optimize sufficiently simple practical problems at a reasonable cost, given the current accessibility of cloud computing resources. Ongoing advances in distributed computing are likely to bring more design problems within the reach of deterministic global optimization methods, yielding rigorous guaranteed solutions in the presence of practical manufacturing constraints.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1367-2630/ab2e19
