Data analytic approach for calibration of online photovoltaic cell metrology

光伏电池用于利用太阳能并将其转换为电能，晶硅光伏电池是使用的主要技术。正在生产的光伏电池的质量控制很难实时完成，因为这些电池是在高通量过程中生产的。然而，有一些方法可用于离线测试电池，其中测量方块电阻是最常见的测量技术。Aurora Solar开发了一种基于红外反射仪的在线(即实时)传感器，用于在生产过程中监控光伏电池的质量。然而，加工条件的变化可能会影响测量以及它们与离线测量的比较。因此，本项目的目的是提高在线传感器的稳健性，并对其进行校准，使其能够在所有可能的工艺条件下获得等效的离线方块电阻测量。将对工业获得的数据应用先进的数据分析技术，以开发稳健的校准算法。

Photovoltaic (PV) cells are used to harness solar energy and convert it to electricity, and crystalline silicon PV cells are the major technology being used. Quality control of the PV cells being produced is challenging to accomplish in real time, since these cells are produced in a high-throughput process. However, there are some methods available for offline testing of the cells, with measurement of the sheet resistance with the four point probe being the most common measurement technique.Aurora Solar has developed an inline (i.e. real time) sensor based on infrared reflectometry for monitoring quality of the PV cells during production. However, variations in processing conditions can impact the measurements and their comparison with offline measurements. Therefore, the aim of this project is to improve robustness of the inline sensor and calibrate it to be able to obtain the equivalent offline sheet resistance measurement under all possible process conditions. Advanced data analysis techniques will be applied on industrially obtained data in order to develop a robust calibration algorithm.