UC San Diego

Perovskite materials are emerging as promising top-cell candidates for high-efficiency silicon tandem solar cells due to their sharp absorption onsets, long carrier lifetimes/diffusion lengths, and tunable bandgaps. However, to achieve commercialization, challenges such as their metastability in operating conditions and their irreproducibility due to sensitivity to process variance must be addressed. Hence the Perovskite Automated Spin Coat Assembly Line – PASCAL – is introduced as a materials acceleration platform for the deposition and characterization of spin-coated thin films, with specific application to halide perovskites. We first demonstrate improved consistency of perovskite film fabrication by tightly controlling process parameters uniquely exposed under the automated experimental framework. Next, we conduct 3 case studies using PASCAL to 1) conduct composition screening spanning the triple-cation, triple-halide composition space, producing a machine learning assisted composition with film durability under intense illumination and heat. 2) Leverage PASCAL to conduct series of experiments to conformally coat perovskite on commercially textured silicon enabling a tandem device prototype. 3) Perform standardized tests on PASCAL-produced thin films using a Bayesian optimization framework to accelerate durability research and develop regression models linking PASCAL's cost-effective metrology to costly standard tests. The approach, hardware, and data detailed herein highlight automated platforms as an opportunity to accelerate the identification and discovery of novel thin film materials and demonstrates the efficacy of PASCAL specifically for automation of solution-processed optoelectronic thin film research.