dc.contributor.author |
Ayesha, Razi |
|
dc.date.accessioned |
2023-08-21T10:00:53Z |
|
dc.date.available |
2023-08-21T10:00:53Z |
|
dc.date.issued |
2023-08 |
|
dc.identifier.other |
Reg no. 320772 |
|
dc.identifier.uri |
http://10.250.8.41:8080/xmlui/handle/123456789/37074 |
|
dc.description |
Supervisor Name: Dr. Amna Safdar |
en_US |
dc.description.abstract |
This study explores the use of deep learning to predict the optimal optical design for the top
cell in tandem solar cells, to maximize power conversion efficiency. The study also investigates
the effects of metasurfaces on tandem solar cell architecture, including the use of active layers as
metasurfaces. The study proposes three stacked layers of metasurfaces, each with a specific
function. Computational techniques are developed to analyze the optical responses of
metasurfaces, including using simulation tools and algorithms. The study uses Artificial Neural
Networks (ANN) and 2D Convolutional Neural Networks (CNN) to analyze a dataset of 10,578
TiO2/CH3NH3PBr3/ZnO metasurfaces. Nine different CNN models were used with different
architectures to identify the best hyperparameters that give the low mean square error. The results
show that CNN shows high prediction accuracy of resonator geometry aside from the training
dataset. The CNN-based predictions were generated much faster, taking an average of 0.3 ± 0.05
seconds per prediction, whereas each FDTD simulation (n=9) took approximately 25 minutes
hence, the CNN is 85 times faster than conventional solvers. The Deep SHapley Additive
Explanations (SHAP) framework was used to gain insights into CNN's predictions and understand
the behavior of complex nanophotonic devices. The designed metasurfaces were integrated into a
typical reference tandem solar cell architecture, and the study concludes that the proposed
metasurfaces can significantly enhance the efficiency of tandem solar cells. The active layer
comprises of near 90% absorption of solar spectrum. The average absorption of the top cell
increased in the UV-vis region (650-800nm) up to 93.4%. The bandwidth of absorption in the the
Silicon bottom cell also increased which shows that metasurface transmits or scatters the
unabsorbed NIR light to bottom cell. The estimated Jsc of the top and bottom cell is 19.5mA/cm2
and 20mA/cm2 respectively. The Jsc in the top cell increased by 2mA/cm2 by integrating
optimized metasurface with the bottom cell. The recorded Voc of the solar cell is 0.7397V. The
total Jsc of 35.91 mA/cm2
. The fill factor observed is 82.2% |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
School of Chemical and Material Engineering (SCME), NUST |
en_US |
dc.subject |
: Tandem solar cells, Metasurfaces, Deep Learning, Convolutional Neural Networks, Numerical modeling, and simulation |
en_US |
dc.title |
Optimization of Tandem Solar cells by Deep Learning |
en_US |
dc.type |
Thesis |
en_US |