  Process optimization of photovoltaic devices is a time-intensive, trial and
error endeavor, without full transparency of the underlying physics, and with
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 that identifies the root cause(s) of
underperformance with layer by-layer resolution and reveals alternative optimal
process windows beyond global black-box optimization. Our Bayesian-network
approach links process conditions to materials descriptors (bulk and interface
properties, e.g., bulk lifetime, doping, and surface recombination) and device
performance parameters (e.g., cell efficiency), using a Bayesian inference
framework with an autoencoder-based surrogate device-physics model that is 100x
faster than numerical solvers. With the trained surrogate model, our approach
is robust and reduces significantly the time consuming experimentalist
intervention, even with small numbers of fabricated samples. To demonstrate our
method, we perform layer-by-layer optimization of GaAs solar cells. In a single
cycle of learning, we find an improved growth temperature for the GaAs solar
cells without any secondary measurements, and demonstrate a 6.5% relative
AM1.5G efficiency improvement above baseline and traditional black-box
optimization methods.
