The impact of interstitial Fe contamination on n-type Cz-Silicon for high efficiency solar cells

Hajjiah, Ali; Soha, M.; Gordon, Ivan; Poortmans, Jozef; John, Joachim

doi:10.1016/j.solmat.2020.110550

Cited by 6 publications

(1 citation statement)

References 32 publications

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“…Kutsukake et al, [32,33] evaluated the influence of process parameters on the interstitial oxygen (Oi) concentration in CZ-grown Si crystals based on the virtual experiments of CZ growth of silicon using CNN-based ML. Hajjiah et al, [34] investigated the influence of Fe contamination on the effective minority carrier lifetime of n-type CZ silicon bulk material used for high efficiency solar cells. Dang et al, [35] studied the optimization of the controlling recipe in quasi-single crystalline silicon growth using artificial neural network and genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Exploring mc‐Silicon Wafers: Utilizing Machine Learning to Enhance Wafer Quality Through Etching Studies

Raji,

Balakrishnapillai Suseela,

Manikkam

et al. 2024

Cryst. Res. Technol.

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This paper provides a method for improving the photovoltaic conversion efficiency and optical attributes of silicon solar cells manufactured from as‐cut boron doped p‐type multi‐crystalline silicon wafers using acid‐based chemical texturization via machine learning. A decreased reflectance, which can be attained by the right chemical etching conditions, is one of the key elements for raising solar cell efficiency. In this work, the mc‐Silicon wafer surface reflectance is obtained under (<2%) after optimization of wet chemical etching. The HF + HNO3 + CH3COOH chemical etchant is used in the ratio 1:3:2 at different conditions of the etching duration of 1 min, 2 min, 3 min, and 4 min, respectively. The as‐cut boron doped p‐type mc‐silicon wafers are analysed with ultraviolet–visible spectroscopy, optical microscopy, Fourier transforms infrared spectroscopy, thickness profilometer, and scanning electron microscopy before and after etching. The chemical etching solution produces good results in 3 min etched wafer, with a reflectivity value of <2%.The reflectivity and optical images are inputs to the convolutional neural network model and the linear regression model to obtain the etching rate for better reflectivity. The classification model provides 99.6% accuracy and the regression model results in the minimum mean squared error (MSE) of 0.062.

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