Improving Solar Cell Metallization Designs using Convolutional Neural Networks
This work addresses the challenge of improving solar cell efficiency for renewable energy applications, representing an incremental advancement by building upon existing Topology Optimization techniques with deep learning integration.
The paper tackled the problem of optimizing solar cell metallization designs by introducing SolarNet, a CNN-based reparameterization scheme that modifies the optimization domain to improve performance over traditional Topology Optimization methods, demonstrating enhanced results across various solar cell shapes and busbar geometries.
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.