  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, we present a global deep learning-based
inverse design framework, 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. We demonstrate 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. Our 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.
