Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

光伏设备的工艺优化是一项耗时，反复试验的工作，缺乏底层物理的完全透明性，并且依赖于用户施加的约束，这些约束可能会或可能不会导致全局最优。本文中，我们证明了将物理领域知识嵌入贝叶斯网络中可以实现砷化镓（GaAs）太阳电池的优化方法，该方法以逐层分辨率识别性能不佳的根本原因，并揭示了传统方法以外的其他最佳工艺窗口黑盒优化。我们的贝叶斯网络方法将关键的GaAs工艺变量（生长温度）链接到材料描述符（本体和界面属性，例如，本体寿命，掺杂和表面重组）和器件性能参数（例如，电池效率）。以此目的，我们将贝叶斯推理框架与神经网络代理设备物理模型相结合，该模型比数值求解器快100倍。通过训练有素的代理模型和少量的实验样本，我们的方法大大减少了实验人员所需的耗时干预和表征。作为我们方法的证明，在仅五次金属有机化学气相沉积中，我们确定了GaAs太阳能电池的窗口，块体和背面场层的优越的生长温度曲线，而无需进行任何二次测量，结果证明了6.5％与传统的网格搜索方法相比，AM1.5G的相对效率有所提高。通过训练有素的代理模型和少量的实验样本，我们的方法大大减少了实验人员所需的耗时干预和表征。作为我们方法的证明，在仅五次金属有机化学气相沉积中，我们确定了GaAs太阳能电池的窗口，块体和背面场层的优越的生长温度曲线，而无需进行任何二次测量，结果证明了6.5％与传统的网格搜索方法相比，AM1.5G的相对效率有所提高。通过训练有素的代理模型和少量的实验样本，我们的方法大大减少了实验人员所需的费时的干预和表征。作为我们方法的证明，在仅五次金属有机化学气相沉积中，我们确定了GaAs太阳能电池的窗口，块体和背面场层的优越的生长温度曲线，而无需进行任何二次测量，结果证明了6.5％与传统的网格搜索方法相比，AM1.5G的相对效率有所提高。