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Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

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Published on: May 22, 2015

数据驱动的道氧化物被动接触太阳能电池性能分析使用机器学习.

Jiakai Zhou^1,2,3,4, T Jesper Jacobsson^1,2,3,4, Zhi Wang⁵

¹Institute of Photoelectronic Thin Film Devices and Technology of Nankai University, Nankai University, Tianjin, 300350, China.

Advanced materials (Deerfield Beach, Fla.)

|January 4, 2024

概括

此摘要是机器生成的。

道氧化物被动接触 (TOPCon) 太阳能电池表现越来越好,但数据共享的挑战仍然存在. 分析揭示了多样化的制造业,并确定了未来太阳能发展的关键特征.

关键词:

在TOPCon上的TOPCon.加速设计是一种加速设计.机器学习是机器学习.太阳能电池是太阳能电池中的一个.

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科学领域:

材料科学材料科学材料科学
可再生能源是可再生能源的来源.
半导体物理半导体物理

背景情况:

道氧化物被动接触器 (TOPCon) 对于提高太阳能电池效率至关重要.
光伏国际技术路线图将TOPCon确定为一个关键的未来技术.
关于细胞发育和广泛采用TOPC的挑战仍然存在.

研究的目的:

编译和分析TOPCon太阳能电池的现有文献数据.
确定TOPCon细胞制造中的趋势和最佳实践.
为了揭示制造参数和细胞性能之间的关系.

主要方法:

从131篇论文中收集了405个TOPCon设备的文献数据.
对设备性能指标的统计分析.
机器学习算法用于识别性能驱动特征.

主要成果:

一个令人惊的小数据集突出了TOPCon领域数据传播的问题.
制造程序的显著多样性表明缺乏标准化的最佳实践.
性能趋势显示逐渐改善,表明持续优化.

结论:

分析加强了有关TOPCon设备架构性能的现有假设.
影响性能的非直观特征组合被确定为进一步研究.
该研究旨在促进TOPCon研究社区更好的数据管理和共享.