The capabilities of modern precision nanofabrication and the wide choice of materials [plasmonic metals, high-index dielectrics, phase change materials (PCM), and 2D materials] make the inverse design of nanophotonic structures such as metasurfaces increasingly difficult. Deep learning is becoming increasingly relevant for nanophotonics inverse design. Although deep learning design methodologies are becoming increasingly sophisticated, the problem of the simultaneous inverse design of structure and material has not received much attention. In this contribution, we propose a deep learning-based inverse design methodology for simultaneous material choice and device geometry optimization. To demonstrate the utility of the proposed method, we consider the topical problem of active metasurface design using PCMs. We consider a set of four commonly used PCMs in both fully amorphous and crystalline material phases for the material choice and an arbitrarily specifiable polygonal meta-atom shape for the geometry part, which leads to a vast structure/material design space. We find that a suitably designed deep neural network can achieve good optical spectrum prediction capability in an ample design space. Furthermore, we show that this forward model has a sufficiently high predictive ability to be used in a surrogate-optimization setup resulting in the inverse design of active metasurfaces of switchable functionality.
State-of-the-art nanofabrication permits the realization of highly aligned multi-layered metasurfaces with high lateral resolution and wide areas. The exploitation of the vast degrees of freedom and material choice is hampered by the difficulty in the inverse design of metasurfaces. The prevalent design approach of unit-cell library creation and element juxtaposition is known to result in reduced efficiency owing to the inaccurate accounting of inter-element coupling. We report on our recent efforts in accelerated evolutionary optimization for designing metasurfaces with extended unit-cells using learned surrogate models. The difficulty in creating learned models with acceptable predictive capacity in higher dimensional parameter spaces arises from the need for extensive ground-truth generation. By a systematic study of network architectures and dataset sampling strategies, we uncover efficient ground-truth generation strategies. Specifically, we consider 2 and 3-nanoellipse titania metaatoms allowing full control over the elliptical parameters and with an optical response consisting of the spectral behavior of various transmission and reflection-mode diffracted orders for proof-of-concept demonstration. The systematic investigation reveals that densely connected neural architecture and judicious sampling strategies can allow learned model creation even with smaller ground-truth datasets.
All-dielectric metasurface (ADM) color filter arrays (CFA) are attractive for replacing conventional organic-dye color filters due to several advantages such as CMOS-compatibility, size scalability, and robustness to degradation. We propose metasurface designs with polygon-shaped unit cells for improving the performance of such filters. Our numerical results predict improvement in color purity for red, green, and blue primary color filters and significant reduction in insertion loss for blue filters as compared to the previously reported designs using simple circular shapes. The proposed differential evolution optimization methodology can be useful for improving the performance of other metasurface spectral filtering structures.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.