  Metasurfaces have received a lot of attentions recently due to their
versatile capability in manipulating electromagnetic wave. Advanced designs to
satisfy multiple objectives with non-linear constraints have motivated
researchers in using machine learning (ML) techniques like deep learning (DL)
for accelerated design of metasurfaces. For metasurfaces, it is difficult to
make quantitative comparisons between different ML models without having a
common and yet complex dataset used in many disciplines like image
classification. Many studies were directed to a relatively constrained datasets
that are limited to specified patterns or shapes in metasurfaces. In this
paper, we present our SUTD polarized reflection of complex metasurfaces
(SUTD-PRCM) dataset, which contains approximately 260,000 samples of complex
metasurfaces created from electromagnetic simulation, and it has been used to
benchmark our DL models. The metasurface patterns are divided into different
classes to facilitate different degree of complexity, which involves
identifying and exploiting the relationship between the patterns and the
electromagnetic responses that can be compared in using different DL models.
With the release of this SUTD-PRCM dataset, we hope that it will be useful for
benchmarking existing or future DL models developed in the ML community. We
also propose a classification problem that is less encountered and apply neural
architecture search to have a preliminary understanding of potential
modification to the neural architecture that will improve the prediction by DL
models. Our finding shows that convolution stacking is not the dominant element
of the neural architecture anymore, which implies that low-level features are
preferred over the traditional deep hierarchical high-level features thus
explains why deep convolutional neural network based models are not performing
well in our dataset.
