Design investigation on 100 μm-thickness thin silicon PERC solar cells with assistance of machine learning

Thin crystalline silicon passivated emitter and rear cell (PERC) solar cells are a very prospective technology for next-phase photovoltaic development due to the potential of high cost effectiveness. The reduction of silicon wafer thickness can significantly save the costs, but there is a loss of cell efficiency if cell design is not conducted. For the thinned 100 μm-thickness PERC solar cells without design, the efficiency loss is pronounced from commercial 180 μm-thickness. In this paper, we have designed and optimized SiO2/SiNx/SiNx/SiOx thin films (here two SiNx layers have different refractive index) on the front surface and SiNx/SiOx thin films on the back surface for the standard front single-sided textured PERC cells. Based on this, we further design and investigate the case of double-sided textured PERC solar cells. Compared with the reference cell, the present designs can lead to the short-circuit current density increase by 0.6 mA/cm2 and the open-circuit voltage enhancement by 10 mV for the front textured case, which causes the efficiency gain of 0.7% from 21.6% to 22.3%. For the double-sided textured cells, the efficiency has an extra increase of 0.6% from 22.3% to 22.9%. Finally, we have constructed the efficiency prediction model by using the multilayer perceptron algorithm in machine learning. It is found from the SHAP values that a significant effect of the front SiNx thickness is observed to predict the performance of the PERC cells.

Heng Zhu and Wensheng Yan and Yiming Liu and Die Hu and Yiteng Tu and Zhengtao Huang and Xinyu Tan

Materials Science in Semiconductor Processing

Machine learning,Multilayer perceptron algorithm,Optical and electrical designs,Passivated emitter and rear cell (PERC),Solar cells

2022

1

10.1016/J.MSSP.2021.106198

1369-8001

106198

Pergamon

137