Multi-resolution physics-guided optimization of high-efficiency AlGaAs/GaAs solar cells using a digital twin approach

This study presents a physics-guided multi-resolution optimization approach using a digital twin framework for the systematic design of high-efficiency AlGaAs/GaAs single-junction solar cells as photovoltaic power sources. To investigate the strongly coupled design space governing device performance, a global coarse-grid dataset of 10,000 device configurations is generated using an automated PC1D-5 simulation across fabrication-realistic ranges of structural and doping parameters. Global coarse-grid exploration enables identification of high-performance regions, while physics-constrained feasibility screening ensures that only physically meaningful and operationally realistic configurations are retained. Within this feasible design space, global optimization identifies a coarse-grid candidate optimum with a conversion efficiency of 30.708%. Because coarse sampling may fail to resolve narrow performance maxima, localized high-resolution refinement is performed around this candidate, identifying an improved optimal configuration achieving 31.898% efficiency. This corresponds to an additional 1.19 percentage-point increase (approximately 3.9% relative improvement) over the coarse-grid optimum. These results demonstrate that coarse-grid exploration alone is insufficient to capture the true physical optimum in III–V heterojunction solar cells, whereas a coarse-to-fine optimization strategy enables accurate identification of physically meaningful optima with manageable computational cost. The proposed approach provides a practical and scalable simulation-driven methodology for optimizing photovoltaic power-source performance. • Physics-guided multi-resolution optimization of GaAs cells. • Timestep convergence ensures consistent terminal I–V metrics. • Coarse grid (30.7%) refined to 31.9% efficiency. • 3.9% relative gain via localized high-resolution search. • Design-stage GaAs performance under controlled assumptions.