DOI: 10.1002/pip.3779 ISSN: 1062-7995

A collaborative framework for unifying typical multidimensional solar cell simulations – Part I. Ten common simulation steps and representing variables

Fa‐Jun Ma, Shaozhou Wang, Chuqi Yi, Lang Zhou, Ziv Hameiri, Stephen Bremner, Xiaojing Hao, Bram Hoex
  • Electrical and Electronic Engineering
  • Condensed Matter Physics
  • Renewable Energy, Sustainability and the Environment
  • Electronic, Optical and Magnetic Materials


Multidimensional simulations for diverse solar cells often encounter distinctive configurations, even when employing the same simulation software. The complexity and inefficiency of this process are further exacerbated when employing different simulators. From our extensive decade‐long experience in numerical simulations of diverse solar cells, we have identified ten common simulation steps intrinsic to typical electrical and optical simulations. Subsequently, we propose ten sets of variables that encompass all the relevant details required for these steps. To address the challenge of varying information requirements for each variable across different simulations, we assign a list, a versatile data type, to each variable. This approach, by design, enables concise, coherent, and flexible input, accommodating the unique demands of each simulation. However, to ensure unambiguous simulations, precise specifications for these variables are essential. Computer code has been successfully implemented to ensure adherence to specifications and expedite variable synchronization with Sentaurus, the de facto standard for device simulation. Within this framework, users are only tasked with editing variables in a plain text file, obviating the need for in‐depth knowledge of Sentaurus. This streamlines the prerequisites for engaging in numerical simulation significantly. Through thoughtful design considerations, we preserve the simulation capacity while simultaneously enhancing productivity considerably. This open‐source framework welcomes global collaboration within the photovoltaic community and has the potential to generate an extensive dataset for cost‐effective artificial intelligence training.

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