Statistical insights from inline solar cell metrology data in a PERC production environment

Johnson Wong, Bernhard Mitchell, Sascha Esefelder, Britta Mette, Budi Tjahjono, Kwan Bum Choi, Jian Wei Ho & Gordon Deans
Aurora Solar Technologies Inc., North Vancouver, Canada; WAVELABS Solar Metrology Systems GmbH, Leipzig, Germany; Sino- American Silicon (SAS) Products Ltd, Hsinchu, Taiwan; Solar Energy Research Institute of Singapore (SERIS), Singapore
The adaptation of solar cell physics models and advanced laboratory- based measurement techniques to enable their use in high-volume, inline solar cell production settings is an exciting development towards implementing Industry 4.0 compliant smart solar cell factories. This paper outlines how a blend of physics-based analysis and statistical data science methods can aid continuous improvement and yield optimization in high-volume solar cell fabrication. A specific example is provided for a passivated emitter, rear locally contacted (PERC) solar cell production environment, where four batches of 500 commercial solar cells are evaluated using I–V at one-Sun as well as both contacted and contactless spectral response techniques. The spectral response techniques revealed prominent periodic patterns in the cell measurement sequence, which could be traced to the anti-reflection coating deposition process. This process inhomogeneity led to bimodal distributions in each batch with an efficiency difference as large as 0.07% between the modes. Thus, its identification by the spectral response technique is an important first step towards improving the efficiency distribution via deposition uniformity improvement. A yield- oriented cell physics model is used to interpret the various data in the context of underlying cell parameters, forming the basis for previously impractical root cause analysis in complex adverse events, and for process optimization in order to obtain sustained yield improvement in high-volume production.

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English Notes

  • Principles of IR Reflectometry for the Measurement of Doped Layers in Silicon Wafers
    • Summary: Infrared (IR) Reflectometry is a non-contact method for rapid characterization of doped layers in silicon wafers. It is used in industrial PV cell production for monitoring and control of the emitter fabrication process. This application note describes the scientific principles behind this technique, and its relationship and advantages compared to the commonly accepted fourpoint probe characterization method.
  • Using IR Reflectometry in PERC Photovoltaic Cell Fabrication Process Control
    • Summary: This application note discusses and provides guidelines on the application of statistical process control (SPC) and engineering process control (EPC) for achieving optimal control of phosphorus emitter diffusion profiles in PERC cell production, thus driving higher production line yield and profit. Two relevant properties that are produced during diffusion, the emitter sheet resistance and surface concentration, are compared for diffusion furnace monitoring and control. Surface concentration has characteristics that makes it better for active feedback control in EPC. It is also more directly related to finished device parameters like open-circuit voltage (Voc) and fill factor (FF). The Aurora Solar Technologies DMTM IR Reflectometry technology can measure emitter surface concentration, making the use of this property practical in diffusion furnace monitoring and process control.

Chinese Notes

  • 红外线反射法原理在硅片掺杂层中的应用
    • 摘要红外线反射法是一种非接触式的测量技术,用于快速测量硅片中掺杂层。在量产太阳电池的时候,监控发射极制造过程。本使用者指南解释该技术背后的科学原理,以及其与四探针测量相比之下的关联及优势。
  • 红外线反射法在于磷扩散过程控制的应用
    • 摘要此使用者指南提供运用统计过程控制以及主动反馈控制的方式,以达成在 PERC 电池制作中对磷扩散发射器曲线的最佳控制,进而提升生产线产能。此文章针对红外线反射法可以监控的两个参数—掺杂层的表面浓度与及方阻, 并比较这两种参数用于过程控制的有效性。相比之下,掺杂层的表面浓度具有较佳的统计特性,对于主动反馈控制有明显的优势 。同时, 表面浓度与完成的太阳能电池的质量更有直接的关联。表面浓度与金属复合、接触电阻和发射极饱和电流密度息息相关,这使其有潜力成为追踪和使用统计程序控制的重要指标。因此,红外线反射法可用于了解掺杂层受到质量影响的特性,这比起使用四探针测量更能有效控管品质及流程。


R. Evans, J. Wong, G. Deans, Control of Manufacturing Variations in Emitter Resistivity to Increase PERC Solar Cell Performance, SNEC 12th (2108) International Photovoltaic Power Generation and Smart Energy Conference, Shanghai, May 2018

S. Baker-Finch, R. Evans, B. Eggleston, E.C. Ong, H. Naidu, A. Turner, V. Prajapati, M.E. Ooi, D. Suwito, M. Mrosko, I. Kutscher, Ramping Advanced Silicon Solar Cell Production with Virtual Wafer Tracking, 26th Workshop on Crystalline Silicon Solar Cells & Modules: Materials and Processes, Vail, Colorado, August 2016