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

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.