Recently, deep learning methods have revolutionized the design of nanophotonic devices, which provides a new
way to efficiently design nanophotonic devices. Here, we demonstrated a deep learning method using attention
mechanisms to inverse design nanophotonic devices, the mean relative error of the predicted value can be as low
as 4.1% or less. Using the encoder part of Transformer, the long sequence of spectral data can be mapped to the
structural parameters of the nanorod hyperbolic metamaterial. The inverse design model based on the attention
mechanisms is good at processing sequence data and can be calculated in parallel, which is an effective way to
design nanophotonic devices.
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