  Optimizing the design of solar cell metallizations is one of the ways to
improve the performance of solar cells. Recently, it has been shown that
Topology Optimization (TO) can be used to design complex metallization patterns
for solar cells that lead to improved performance. TO generates unconventional
design patterns that cannot be obtained with the traditional shape optimization
methods. In this paper, we show that this design process can be improved
further using a deep learning inspired strategy. We present SolarNet, a
CNN-based reparameterization scheme that can be used to obtain improved
metallization designs. SolarNet modifies the optimization domain such that
rather than optimizing the electrode material distribution directly, the
weights of a CNN model are optimized. The design generated by CNN is then
evaluated using the physics equations, which in turn generates gradients for
backpropagation. SolarNet is trained end-to-end involving backpropagation
through the solar cell model as well as the CNN pipeline. Through application
on solar cells of different shapes as well as different busbar geometries, we
demonstrate that SolarNet improves the performance of solar cells compared to
the traditional TO approach.
