Lightsail design with neural optimization of topology
L. Norder (TU Delft - Mechanical Engineering)
M.A. Bessa – Mentor (TU Delft - Team Georgy Filonenko)
R. A. Norte – Graduation committee member (TU Delft - QN/Groeblacher Lab)
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Abstract
Conventional Topology
Optimization (TO) enables the inverse design of nanophotonic structures by specifying the objective
and constraints without a predefined topological concept. Yet, extreme scenarios such as the design
of a lightsail pose challenges that require new solutions. Here, a
convolutional neural network (CNN) based TO methodology is extended to optimize a
two-dimensional photonic crystal used to design a lightsail that aims to reach the
nearest star (Alpha Centauri) within 20 years by achieving 20% of the speed of light. The CNN-TO
performance is compared to a more conventional method of moving asymptotes (MMA) based TO by
optimizing a photonic crystal unit-cell for the 2016 Starshot Initiative parameters. The
CNN-TO requires up to 40% fewer iterations than MMA-TO to reach better performance under different
operational conditions. The generated design turned out to be easy to fabricate, allowing them to be
produced with optical lithography. Additionally, a study regarding the design challenges of
the lightsail has been performed,
which resulted in an optimization considering the
functionality of the sail. Additionally,
the study showed the sensitivity of the resulting
design to varying objectives and materials. Therefore, underlining the necessity of
considering multiple operating conditions (e.g. laser alignment and cooling) within the
design process.