A Synergistic Process Optimization and Data-Driven Modeling Strategy for Unraveling and Enhancing the Low-Light Response in Back-Contact Solar Cells.

The interdigitated back contact (IBC) structure offers high efficiency potential for crystalline silicon solar cells, yet its low-light performance (LLP) faces ongoing debates. This study systematically investigates LLP mechanisms in back contact (BC) cells via process optimization, simulation, and machine learning (ML). We identify leakage paths caused by residual "cap-shaped" borosilicate glass in rear p-type poly-Si regions as a critical bottleneck. By optimizing laser grooving through gap adjustments and additive engineering, the LLP of tunnel oxide passivated contact (TBC) cells is elevated to match that of TOPCon cells, reducing power loss to below 5%. Using SunSolve and Quokka3 simulations, we analyze module-level double-diode parameters and device-to-module losses. ML-driven data mining reveals an inherent trade-off between LLP and power conversion efficiency (PCE). To resolve this, we develop a multi-head neural network integrated with an evolutionary algorithm for co-optimization. This yields a candidate parameter set that effectively balances PCE and LLP across both TOPCon and TBC solar cells. Our work clarifies the microscopic origins of LLP limitations and provides a practical framework for designing high-efficiency BC cells with superior low-light response, accelerating industrial application.