在工作条件下对硅模块中Na+扩散的见解:用D-SIMS测量低浓度

Jacob Clenney, Erick M Loran, G. von Gastrow, D. Fenning, R. Meier, M. Bertoni
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引用次数: 1

摘要

电场下的钠诱导分流是晶体硅太阳能组件可靠性的一个挑战。这种电势诱导的分流型(PID-s)退化的来源已经被很好地理解[1],它对功率损耗的影响已经基于电池或模块水平的现象模型进行了深入研究,该模型将实验功率损耗与应力参数(时间、温度、电压)联系起来[1]。然而,在微观水平上,对导致PID的Na离子迁移动力学及其与效率退化的定量关系知之甚少。本文用动态二次离子质谱(D-SIMS)研究了钠离子在乙烯-醋酸乙烯酯(EVA)和硅中的迁移。每个样品在60-90°C的现场相关温度下退火,以解决常见光伏装置的典型迁移机制。对SIMS迁移剖面的分析显示,Na在EVA中的扩散系数为D0,EVA = 0.09±0.14 cm2/s,活化能EA,EVA = 0.85±0.04 eV,扩散系数高于硅中的外推文献值(D0,Si =(3.03±2.42)x10−5 cm2/s, EA,Si = 0.98±0.02 eV)。新的见解将包括在一个基于漂移-扩散的退化模型中,该模型考虑了所有相关界面的分配系数。该模型可以根据给定的模堆和Na+在每种材料中的扩散情况,帮助预测现场pid失效。该工具可用于工艺优化和材料选择,大大降低了技术验证的成本和时间。
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Insights into Na+ Diffusion in Silicon Modules under Operating Conditions: Measuring Low Concentrations by D-SIMS
Sodium induced shunting under an electric field is a challenging reliability issue in crystalline Si solar modules. THe source of this Potential-Induced Degradation of the Shunting type (PID-s) is well understood [1] and its influence on power loss has been intensively studied based on phenomenological models on cell or module level relating the experimental power-loss to stressing parameters (time, temperature, voltage) [1]. However, little is known about the Na ion migration kinetics, responsible for PID on a microscopic level, and its quantitative relation to the efficiency degradation. In this paper we present our investigations of sodium ion migration in Ethylene-Vinyl Acetate (EVA) and silicon through Dynamic Secondary Ion Mass Spectroscopy (D-SIMS). Each sample was annealed at field relevant temperatures from 60–90 °C to address typical migration mechanisms of common PV installations. Analysis of the SIMS migration profiles revealed a diffusivity constant D0,EVA = 0.09 ± 0.14 cm2/s and an activation energy EA,EVA = 0.85 ± .04 eV for Na in EVA and diffusivities higher than extrapolated literature values in silicon (D0,Si = (3.03 ± 2.42)x10−5 cm2/s, and EA,Si = 0.98 ± 0.02 eV). The new insight will be included in a drift-diffusion based degradation model accounting for the partition coefficient across all relevant interfaces. This model can assist in predicting PID-failure in the field based on the given mudle stack and the diffusion of Na+ through each material. This tool can be used for process optimization as well as material selection significantly reducing the cost and time to validate a technology.
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