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A combined Statistical and TCAD Model as a method for understanding and reducing variations in multicrystalline Si nsolar cell production

机译:一种组合的统计和TCAD模型作为理解和减少多晶硅NSOLAR细胞生产变化的方法

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Monitoring the I-V parameters in mass production yields a distribution that cannot be understood in a simple manner. For example, if V_(oc) varies greatly, it is not obvious whether this is mainly due to variations in the bulk lifetime or in the surface passivation or due to other sources. In this work, we develop a method where statistics is combined with numerical device modeling to obtain a physical interpretation of the observed variations. In the first part, we derive a multivariate statistical model to extract the main influences of fabrication fluctuations on the I-V parameters. This statistical model is based on cell parameters measured on a representative sample of solar cells from production. In the second part, we develop a computer-aided design (TCAD) device simulation model for multicrystalline Si solar cells. This TCAD model quantifies the I-V variations on a physically sound basis. However, the number of simulations is grossly reduced by feeding in solely the main influences obtained from the statistical model. In the third part, we verify this method by comparing the calculated distribution with production data. This model is used for optimization strategies for higher cell efficiency, smaller variations in cell parameters and improved yield in mass production. Furthermore, we will apply our methodology to advanced cell concepts. It will allow the early consideration of production fluctuation in device simulation of advanced cell concepts, and therefore a realistic assessment of such concepts.
机译:监视大规模生产中的I-V参数产生的分布不能以简单的方式理解。例如,如果V_(OC)变化很大,这是不明显的,这主要是由于体寿命的变化或表面钝化或由于其他来源。在这项工作中,我们开发一种方法,其中统计与数值设备建模组合以获得观察到的变化的物理解释。在第一部分中,我们推导了多元统计模型,以提取制造波动对I-V参数的主要影响。该统计模型基于从生产的太阳能电池的代表性样本测量的细胞参数。在第二部分中,我们开发了一种用于多晶硅Si太阳能电池的计算机辅助设计(TCAD)设备仿真模型。此TCAD模型会对物理声音的基础定量I-V变量。然而,通过完全喂养从统计模型获得的主要影响,模拟的数量是严重降低的。在第三部分中,我们通过将计算的分布与生产数据进行比较来验证此方法。该模型用于优化策略,用于更高的电池效率,细胞参数的变化较小,并提高了大规模生产的产量。此外,我们将把我们的方法应用于高级细胞概念。它将允许早期考虑生产高级细胞概念的设备模拟中的生产波动,因此对这种概念的现实评估。

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