工藝優化對於實現材料和器件性能的最大化至關重要。光伏器件由於具有複雜的多層結構，受到很多工藝參數的影響，因而對工藝優化更加敏感。傳統的器件優化方法，如貝葉斯網絡、格點搜索和粒子群算法等都是基於黑盒子的優化模式，即直接建立工藝參數和器件性能的關聯。然而，該類方法卻由於缺少物理支撐，一方面強烈依賴於參數範圍的選擇，很難得到全局最優的結果，另一方面也很難找出現有器件瓶頸的本質原因。
來自麻省理工學院和新加坡國立大學由Tonio Buonassisi領導的聯合團隊，變革了以往完全基於黑盒子模式的光伏器件優化方法。他們以材料物理性能為橋樑，將工藝參數和器件性能連接起來，構建了基於貝葉斯網絡的工藝優化新方法。一方面將材料的物理性能（如體材料的摻雜度和界面的載流子複合率）等作為約束條件引入貝葉斯網絡，將其與工藝參數（如生長溫度）耦合起來。另一方面建立了一個基於神經網絡的代理器件物理模型，根據材料性能預測實際器件的效率。通過將訓練好的代理模型與貝葉斯網絡結合，實現光伏器件的逐層優化。值得注意的是，該研究所提出的代理器件物理模型相比傳統的器件性能計算方法效率高100倍以上。他們將該方法具體應用於砷化鎵太陽能電池的工藝參數優化中。結果表明，該方法僅通過五組氣相沉積實驗就能夠給出電池中每一層對應的最優薄膜生長溫度，由此得到的器件效率相比傳統方法高6.5%。該優化方法相比傳統的基於黑盒子模式的器件優化方法不僅可以大幅節省所需的實驗數據，同時也更為準確。由於目前很多能源及功能器件都是基於黑盒子模型來實現器件優化，因而該研究所提出的方法具有一定的共性，有望推廣應用於熱電、電池及晶體管等其他器件的優化。
該文近期發表於npj Computational Materials 6: 9 (2020)，英文標題與摘要如下，點擊https://www.nature.com/articles/s41524-020-0277-x可以自由獲取論文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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