npj: 有物理头脑的贝叶斯网络—太阳能电池工艺的创新优化

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工艺优化对于实现材料和器件性能的最大化至关重要。光伏器件由于具有复杂的多层结构，受到很多工艺参数的影响，因而对工艺优化更加敏感。传统的器件优化方法，如贝叶斯网络、格点搜索和粒子群算法等都是基于黑盒子的优化模式，即直接建立工艺参数和器件性能的关联。然而，该类方法却由于缺少物理支撑，一方面强烈依赖于参数范围的选择，很难得到全局最优的结果，另一方面也很难找出现有器件瓶颈的本质原因。

来自麻省理工学院和新加坡国立大学由Tonio Buonassisi领导的联合团队，变革了以往完全基于黑盒子模式的光伏器件优化方法。他们以材料物理性能为桥梁，将工艺参数和器件性能连接起来，构建了基于贝叶斯网络的工艺优化新方法。一方面将材料的物理性能（如体材料的掺杂度和界面的载流子复合率）等作为约束条件引入贝叶斯网络，将其与工艺参数（如生长温度）耦合起来。另一方面建立了一个基于神经网络的代理器件物理模型，根据材料性能预测实际器件的效率。通过将训练好的代理模型与贝叶斯网络结合，实现光伏器件的逐层优化。值得注意的是，该研究所提出的代理器件物理模型相比传统的器件性能计算方法效率高100倍以上。他们将该方法具体应用于砷化镓太阳能电池的工艺参数优化中。结果表明，该方法仅通过五组气相沉积实验就能够给出电池中每一层对应的最优薄膜生长温度，由此得到的器件效率相比传统方法高6.5%。该优化方法相比传统的基于黑盒子模式的器件优化方法不仅可以大幅节省所需的实验数据，同时也更为准确。由于目前很多能源及功能器件都是基于黑盒子模型来实现器件优化，因而该研究所提出的方法具有一定的共性，有望推广应用于热电、电池及晶体管等其他器件的优化。

该文近期发表于npj Computational Materials 6: 9 (2020)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Zekun Ren, Felipe Oviedo, Maung Thway, Siyu I. P. Tian, Yue Wang, Hansong Xue, Jose Dario Perea, Mariya Layurova, Thomas Heumueller, Erik Birgersson, Armin G. Aberle, Christoph J. Brabec, Rolf Stangl, Qianxiao Li, Shijing Sun, Fen Lin, Ian Marius Peters and Tonio Buonassisi

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.

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