  Nanophotonics finds ever broadening applications requiring complex component
designs with a large number of parameters to be simultaneously optimized.
Recent methodologies employing optimization algorithms commonly focus on a
single design objective, provide isolated designs, and do not describe how the
design parameters influence the device behaviour. Here we propose and
demonstrate a machine-learning-based approach to map and characterize the
multi-parameter design space of nanophotonic components. Pattern recognition is
used to reveal the relationship between an initial sparse set of optimized
designs through a significant reduction in the number of characterizing
parameters. This defines a design sub-space of lower dimensionality that can be
mapped faster by orders of magnitude than the original design space. As a
result, multiple performance criteria are clearly visualized, revealing the
interplay of the design parameters, highlighting performance and structural
limitations, and inspiring new design ideas. This global perspective on
high-dimensional design problems represents a major shift in how modern
nanophotonic design is approached and provides a powerful tool to explore
complexity in next-generation devices.
