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.

中文翻译：

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使用卷积神经网络改善太阳能电池的金属化设计

优化太阳能电池金属化的设计是提高太阳能电池性能的方法之一。近来，已显示拓扑优化（TO）可用于设计太阳能电池的复杂金属化图案，从而提高性能。TO生成了传统形状优化方法无法获得的非常规设计模式。在本文中，我们表明可以使用深度学习启发的策略来进一步改进此设计过程。我们介绍了SolarNet，这是一种基于CNN的重新参数化方案，可用于获得改进的金属化设计。SolarNet修改了优化域，以使CNN模型的权重得以优化，而不是直接优化电极材料的分布。然后，使用物理方程式对CNN生成的设计进行评估，然后再生成用于反向传播的梯度。SolarNet经过端到端培训，涉及通过太阳能电池模型以及CNN管道进行的反向传播。通过在不同形状和不同母线几何形状的太阳能电池上的应用，我们证明，与传统的TO方法相比，SolarNet改善了太阳能电池的性能。