Global Inverse Design across Multiple Photonic Structure Classes Using Generative Deep Learning. Issue 20 (17th July 2021)

• Record Type: Journal Article
• Authors: Yeung, Christopher; Tsai, Ryan; Pham, Benjamin; King, Brian; Kawagoe, Yusaku; Ho, David; Liang, Julia; Knight, Mark W.; Raman, Aaswath P.
• Is Part Of: Advanced optical materials. Volume 9:Issue 20(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-17
• Subjects: deep learning -- generative adversarial networks -- global inverse design -- metasurfaces -- nanophotonics; Optical materials -- Periodicals; Photonics -- Periodicals; 620.11295
• DOI: 10.1002/adom.202100548
• Languages: English
• ISSNs: 2195-1071

Abstract

Abstract: Understanding how nano‐ or micro‐scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge. Photonic metasurfaces, for instance, can be spectrally tuned through material choice and structural geometry to achieve unique optical responses. However, existing numerical design methods require prior identification of specific material−structure combinations, or device classes, as the starting point for optimization. As such, a unified solution that simultaneously optimizes across materials and geometries has yet to be realized. To overcome these challenges, a global deep learning‐based inverse design framework is presented, where a conditional deep convolutional generative adversarial network is trained on colored images encoded with a range of material and structural parameters, including refractive index, plasma frequency, and geometric design. It is demonstrated that, in response to target absorption spectra, the network can identify an effective metasurface in terms of its class, materials properties, and overall shape. Furthermore, the model can arrive at multiple design variants with distinct materials and structures that present nearly identical absorption spectra. The proposed framework is thus an important step towards global photonics and materials design strategies that can identify combinations of device categories, material properties, and geometric parameters which algorithmically deliver a sought functionality. Abstract : A global deep learning‐based inverse design framework is presented, where a conditional deep convolutional generative adversarial network is trained on colored images encoded with a range of material and structural parameters, including refractive index, plasma frequency, and geometric design. In response to target absorption spectra, the network can identify an effective metasurface in terms of its class, materials, and shape.