Pentagonal Photonic Crystal Mirrors: Scalable Lightsails with Enhanced Acceleration via Neural Topology Optimization

Fuente: arXiv
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Main Authors: Norder, L., Yin, S., de Jong, M. J., Stallone, F., Aydogmus, H., Sberna, P. M., Bessa, M. A., Norte, R. A.
Format: Preprint
Published: 2024
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author Norder, L.
Yin, S.
de Jong, M. J.
Stallone, F.
Aydogmus, H.
Sberna, P. M.
Bessa, M. A.
Norte, R. A.
author_facet Norder, L.
Yin, S.
de Jong, M. J.
Stallone, F.
Aydogmus, H.
Sberna, P. M.
Bessa, M. A.
Norte, R. A.
contents The Starshot Breakthrough Initiative aims to send one-gram microchip probes to Alpha Centauri within 20 years, using gram-scale lightsails propelled by laser-based radiation pressure, reaching velocities nearing a fifth of light speed. This mission requires lightsail materials that challenge the fundamentals of nanotechnology, requiring innovations in optics, material science and structural engineering. Unlike the microchip payload, which must be minimized in every dimension, such lightsails need meter-scale dimensions with nanoscale thickness and billions of nanoscale holes to enhance reflectivity and reduce mass. Our study employs neural topology optimization, revealing a novel pentagonal lattice-based photonic crystal (PhC) reflector. The optimized designs shorten acceleration times, therefore lowering launch costs significantly. Crucially, these designs also enable lightsail material fabrication with orders-of-magnitude reduction in costs. We have fabricated a 60 x 60 mm$^2$, 200nm thick, single-layer reflector perforated with over a billion nanoscale features; the highest aspect-ratio nanophotonic element to date. We achieve this with nearly 9,000 times cost reduction per m$^2$. Starshot lightsails will have several stringent requirements but will ultimately be driven by costs to build at scale. Here we highlight challenges and possible solutions in developing lightsail materials - showcasing the potential of scaling nanophotonics for cost-effective next-generation space exploration.
format Preprint
id arxiv_https___arxiv_org_abs_2407_07896
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Pentagonal Photonic Crystal Mirrors: Scalable Lightsails with Enhanced Acceleration via Neural Topology Optimization
Norder, L.
Yin, S.
de Jong, M. J.
Stallone, F.
Aydogmus, H.
Sberna, P. M.
Bessa, M. A.
Norte, R. A.
Optics
Mesoscale and Nanoscale Physics
Machine Learning
Applied Physics
Space Physics
The Starshot Breakthrough Initiative aims to send one-gram microchip probes to Alpha Centauri within 20 years, using gram-scale lightsails propelled by laser-based radiation pressure, reaching velocities nearing a fifth of light speed. This mission requires lightsail materials that challenge the fundamentals of nanotechnology, requiring innovations in optics, material science and structural engineering. Unlike the microchip payload, which must be minimized in every dimension, such lightsails need meter-scale dimensions with nanoscale thickness and billions of nanoscale holes to enhance reflectivity and reduce mass. Our study employs neural topology optimization, revealing a novel pentagonal lattice-based photonic crystal (PhC) reflector. The optimized designs shorten acceleration times, therefore lowering launch costs significantly. Crucially, these designs also enable lightsail material fabrication with orders-of-magnitude reduction in costs. We have fabricated a 60 x 60 mm$^2$, 200nm thick, single-layer reflector perforated with over a billion nanoscale features; the highest aspect-ratio nanophotonic element to date. We achieve this with nearly 9,000 times cost reduction per m$^2$. Starshot lightsails will have several stringent requirements but will ultimately be driven by costs to build at scale. Here we highlight challenges and possible solutions in developing lightsail materials - showcasing the potential of scaling nanophotonics for cost-effective next-generation space exploration.
title Pentagonal Photonic Crystal Mirrors: Scalable Lightsails with Enhanced Acceleration via Neural Topology Optimization
topic Optics
Mesoscale and Nanoscale Physics
Machine Learning
Applied Physics
Space Physics
url https://arxiv.org/abs/2407.07896