Abstract
Design of efficient thin film photovoltaic (PV) cells require optical power absorption to be computed inside a nano-scale structure of photovoltaics, dielectric and plasmonic materials. Calculating power absorption requires Maxwell’s electromagnetic equations which are solved using numerical methods, such as finite difference time domain (FDTD). The computational cost of thin film PV cell design and optimization is therefore cumbersome, due to successive FDTD simulations. This cost can be reduced using a surrogate-based optimization procedure. In this study, we deploy neural networks (NNs) to model optical absorption in organic PV structures. We use the corresponding surrogate-based optimization procedure to maximize light trapping inside thin film organic cells infused with metallic particles. Metallic particles are known to induce plasmonic effects at the metal–semiconductor interface, thus increasing absorption. However, a rigorous design procedure is required to achieve the best performance within known design guidelines. As a result of using NNs to model thin film solar absorption, the required time to complete optimization is decreased by more than five times. The obtained NN model is found to be very reliable. The optimization procedure results in absorption enhancement greater than 200%. Furthermore, we demonstrate that once a reliable surrogate model such as the developed NN is available, it can be used for alternative analyses on the proposed design, such as uncertainty analysis (e.g., fabrication error).
Highlights
Photovoltaic (PV) energy shares in electricity generation have continually grown since the beginning of commercial silicon-based solar cells over 50 years ago [1]
We demonstrated that design optimization of the plasmonic organic thin film photovoltaic cells
We demonstrated that design optimization of the plasmonic organic thin film photovoltaic cells can be efficiently done using a surrogate model, instead of solving costly Maxwell’s electromagnetic can be efficiently done using a surrogate model, instead of solving costly Maxwell’s electromagnetic equations
Summary
Photovoltaic (PV) energy shares in electricity generation have continually grown since the beginning of commercial silicon-based solar cells over 50 years ago [1]. A well-known affect called light trapping, which is mostly achieved by surface patterning of the cell and inducing plasmonic effects, can significantly improve solar light absorption in silicon [3,4] These techniques increase effective optical thickness without increasing the physical thickness of PV material, avoiding undesirable carrier recombination [5]. Precise computational simulators that model electromagnetic equations and material properties at nano-scale and solar optical wavelenghts should be accompanied by powerful optimization algorithms for a feasible and efficient design [7,8,9,10,11,12,13]. The only remedy to such a challenge is the use of “surrogate modeling” This means replacing the black-box (FDTD) simulations with an accurate regression model. Sensitivity analysis is conducted to predict the dependence of the results on small changes in the inputs
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