Quantitative physics-driven framework for evaluating CIGS solar cell simulation tools

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Numerical simulation plays a central role in advancing thin-film photovoltaics. Taking Copper Indium Gallium Selenide (CIGS) as a representative high-complexity use case, this study identifies significant barriers researchers face when translating domain-specific physical logic into quantifiable simulation parameters. We study how existing modeling tools and their underlying features cope with the specific physical requirements of the CIGS domain, such as compositional grading and multi-dimensional transport. We propose a physics-driven evaluation framework to bridge the gap between theoretical requirements and tool proficiency. This framework introduces novel metrics: the Modeling Domain Capability Index, quantifying intrinsic expressiveness, and the Structural Sensitivity Index, capturing the dependence of aggregate capability on assumed coupling order. We benchmark 10 prominent simulation platforms against these metrics alongside an analysis of operational capability (OC)—accessibility, usability, automation, and scalability—combined through the Analytic Hierarchy Process into a Composite Operational Proficiency score. An evidence-anchored scoring rubric and a single-criterion ±1 perturbation analysis are introduced as a reproducibility protocol; the resulting cluster-level taxonomy is shown to be robust to plausible scoring disagreements. Our results show that modeling capability and operational proficiency are only partially correlated: the most expressive platforms—particularly those supporting 3D opto-electrical coupling—offer the broadest physical coverage but demand the greatest expertise and computational resources. By mapping these tools into a unified Physics–OC Landscape, we provide a decision-oriented taxonomy that aligns specific research objectives with the best-suited software architectures without imposing a universal ranking. This study effectively “minds the gap” between the physicist’s modeling needs and the functional reality of the simulation software ecosystem, offering a standardized methodology for benchmarking future scientific software.

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