Darwin at High Temperature: Advancing Solar Cell Material Design Using Defect Kinetics Simulations and Evolutionary Optimization

Abstract

Material defects govern the performance of a wide range of energy conversion and storage devices, including photovoltaics, thermoelectrics, and batteries. The success of large‐scale, cost‐effective manufacturing hinges upon rigorous material optimization to mitigate deleterious defects. Material processing simulations have the potential to accelerate novel energy technology development by modeling defect‐evolution thermodynamics and kinetics during processing of raw materials into devices. Here, a predictive process optimization framework is presented for rapid material and process development. A solar cell simulation tool that models defect kinetics during processing is coupled with a genetic algorithm to optimize processing conditions in silico. Experimental samples processed according to conditions suggested by the optimization show significant improvements in material performance, indicated by minority carrier lifetime gains, and confirm the simulated directions for process improvement. This material optimization framework demonstrates the potential for process simulation to leverage fundamental defect characterization and high‐throughput computing to accelerate the pace of learning in materials processing for energy applications.

The capital intensity of photovoltaics manufacturing: barrier to scale and opportunity for innovation

“…This could reduce constraints on equipment quality and cleanliness, and reduce expenditures on less-deleterious defects. Predictive simulation 58 and process monitoring can be employed to understand the most tightly constrained process variables and opportunities for improvement.…”

Building intuition of iron evolution during solar cell processing through analysis of different process models

“…Cooling is an inherent component of solar cell manufacturing processes, and the precise time-temperature profiles are essential for controlling the post-processing iron distribution and solar cell performance [18,73,75]. For iron evolution during cooling at 10°C/min from 1150 to 500 °C after the heating step discussed in this section, see Online Resource 5.…”

“…See [8,[11][12][13][14][15] for further detail. The chemical state and distribution of iron impurities evolve during high-temperature solar cell processing steps [8,[16][17][18][19] because of the exponential dependence of iron point defect solubility and diffusivity on temperature. As the different states of iron exert varying impacts on minority carrier lifetime [20], accurate modeling of iron evolution is critical to determining its impact on the finished device.…”

Optimization of Solar Cell Production Lines Using Neural Networks and Genetic Algorithms

“…ML has the capacity to learn a wide variety of nonlinear patterns in high-dimensional datasets and can be used to build data-driven models of process intradependencies and interdependencies . ML-based techniques for PV manufacturing have been explored for solar cell material design, optimizing individual processes, and a combination of processes . It has also been explored with regard to DoE optimization, quality control, and troubleshooting with access to wafer tracking .…”