Improving Solar Cell Metallization Designs using Convolutional Neural Networks

TL;DR

This paper introduces SolarNet, a CNN-based method that enhances solar cell metallization design by integrating deep learning with topology optimization, leading to improved performance over traditional methods.

Contribution

The paper presents SolarNet, a novel CNN-based reparameterization scheme that optimizes metallization patterns for solar cells, surpassing traditional topology optimization techniques.

Findings

SolarNet outperforms traditional topology optimization in design quality.

The method is effective across various solar cell shapes and busbar geometries.

End-to-end training enables accurate gradient computation for design improvement.

Abstract

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.