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Register a new userInverse design of lightweight broadband reflector for efficient lightsail propulsion
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Light can exert forces on objects, promising to propel a meter-scale lightsail to near the speed of light. The key to address many challenges in such an ambition hinges on the nanostructuring of lightsails to tailor their optical scattering properties. In this letter, we present a first exhaustive study of photonic design of lightsails by applying large-scale optimization techniques to a generic geometry based on stacked photonic crystal layers. The optimization is performed by rigorous coupled-wave analysis amended with automatic differentiation methods for adjoint-variable gradient evaluations. Employing these methods the propulsion efficiency of a lightsail that involves a tradeoff between high broadband reflectivity and mass reduction is optimized. Surprisingly, regardless of the material choice, the optimal structures turn out to be simply one-dimensional subwavelength gratings, exhibiting nearly 50% improvement in acceleration distance performance compared to prior studies. Our framework can be extended to address other lightsail challenges such as thermal management and propulsion stability, and applications in integrated photonics such as compact mirrors.
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The Starshot lightsail project aims to build an ultralight spacecraft (nanocraft) that can reach Proxima Centauri b in approximately 20 years, requiring propulsion with a relativistic velocity of ~60 000 km/s. The spacecrafts acceleration approach currently under investigation is based on applying the radiation pressure from a high-power laser array located on Earth to the spacecraft lightsail. However, the practical realization of such a spacecraft imposes extreme requirements to the lightsails optical, mechanical, thermal properties. Within this work, we apply adjoint topology optimization and variational autoencoder-assisted inverse design algorithm to develop and optimize a silicon-based lightsail design. We demonstrate that the developed framework can provide optimized optical and opto-kinematic properties of the lightsail. Furthermore, the framework opens up the pathways to realizing a multi-objective optimization of the entire lightsail propulsion system, leveraging the previously demonstrated concept of physics-driven compressed space engineering
We present a gradient-based optimization strategy to design broadband grating couplers. Using this method, we are able to reach, and often surpass, a user-specified target bandwidth during optimization. The designs produced for 220 nm silicon-on-insulator are capable of achieving 3 dB bandwidths exceeding 100 nm while maintaining central coupling efficiencies ranging from -3.0 dB to -5.4 dB, depending on partial-etch fraction. We fabricate a subset of these structures and experimentally demonstrate gratings with 3 dB bandwidths exceeding 120 nm. This inverse design approach provides a flexible design paradigm, allowing for the creation of broadband grating couplers without requiring constraints on grating geometry.
Recent advancements in computational inverse design have begun to reshape the landscape of structures and techniques available to nanophotonics. Here, we outline a cross section of key developments at the intersection of these two fields: moving from a recap of foundational results to motivation of emerging applications in nonlinear, topological, near-field and on-chip optics.
We suggest a broadband optical unidirectional arrayed nanoantenna consisting of equally spaced nanorods of gradually varying length. Each nanorod can be driven by near-field quantum emitters radiating at different frequencies or, according to the reciprocity principle, by an incident light at the same frequency. Broadband unidirectional emission and reception characteristics of the nano-antenna open up novel opportunities for subwavelength light manipulation and quantum communication, as well as for enhancing the performance of photoactive devices such as photovoltaic detectors, light-emitting diodes, and solar cells.
In recent years, the development of nanophotonic devices has presented a revolutionary means to manipulate light at nanoscale. Recently, artificial neural networks (ANNs) have displayed powerful ability in the inverse design of nanophotonic devices. However, there is limited research on the inverse design for modeling and learning the sequence characteristics of a spectrum. In this work, we propose a novel deep learning method based on an improved recurrent neural networks to extract the sequence characteristics of a spectrum and achieve inverse design and spectrum prediction. A key feature of the network is that the memory or feedback loops it comprises allow it to effectively recognize time series data. In the context of nanorods hyperbolic metamaterials, we demonstrated the high consistency between the target spectrum and the predicted spectrum, and the network learned the deep physical relationship concerning the structural parameter changes reflected on the spectrum. Moreover, the proposed model is capable of predicting an unknown spectrum based on a known spectrum with only 0.32% mean relative error. We propose this method as an effective and accurate alternative to the application of ANNs in nanophotonics, paving way for fast and accurate design of desired devices.
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