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Process optimization of photovoltaic devices is a time-intensive, trial anderror endeavor, without full transparency of the underlying physics, and withuser-imposed constraints that may or may not lead to a global optimum. Herein,we demonstrate that embedding physics domain knowledge into a Bayesian networkenables an optimization approach that identifies the root cause(s) ofunderperformance with layer by-layer resolution and reveals alternative optimalprocess windows beyond global black-box optimization. Our Bayesian-networkapproach links process conditions to materials descriptors (bulk and interfaceproperties, e.g., bulk lifetime, doping, and surface recombination) and deviceperformance parameters (e.g., cell efficiency), using a Bayesian inferenceframework with an autoencoder-based surrogate device-physics model that is 100xfaster than numerical solvers. With the trained surrogate model, our approachis robust and reduces significantly the time consuming experimentalistintervention, even with small numbers of fabricated samples. To demonstrate ourmethod, we perform layer-by-layer optimization of GaAs solar cells. In a singlecycle of learning, we find an improved growth temperature for the GaAs solarcells without any secondary measurements, and demonstrate a 6.5% relativeAM1.5G efficiency improvement above baseline and traditional black-boxoptimization methods.

Embedding Physics Domain Knowledge into a Bayesian Network Enables Layer-by-Layer Process Innovation for Photovoltaics