  Meta-devices have gained significant attention and have been widely utilized
in optical systems for focusing and imaging, owing to their lightweight,
high-integration, and exceptional-flexibility capabilities. However, based on
the assumption of local phase approximation, traditional design method neglect
the local lattice coupling effect between adjacent meta-atoms, thus harming the
practical performance of meta-devices. Using physics-driven or data-driven
optimization algorithms can effectively solve the aforementioned problems.
Nevertheless, both of the methods either involve considerable time costs or
require a substantial amount of data sets. Here, we propose a
physics-data-driven approach based "intelligent optimizer" that enables us to
adaptively modify the sizes of the studied meta-atom according to the sizes of
its surrounding ones. Such a scheme allows to mitigate the undesired local
lattice coupling effect, and the proposed network model works well on thousands
of datasets with a validation loss of 3*10-3. Experimental results show that
the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses
a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a
Strehl ratio of 0.94. In contrast to the previous inverse design method, our
method significantly boosts designing efficiency with five orders of magnitude
reduction in time. Our design approach may sets a new paradigm for devising
large-scale meta-devices.
