Abstract
The development of cost-effective solar cells requires on the one hand to master the elaboration techniques, and on the other hand, an adequate design to optimise the photovoltaic efficiency. These two research topics are closely linked and their association in the research work is the key in the development of novel thin film solar cells. The design associated with numerical optimisation gives the set of optimal physical and geometrical parameters, taking into account the technological feasibility. This will allow elaboration to target the most efficient structures in order to speed up the final device realisation. In this work, we used a new approach, based on rigorous multivariate mathematical global Bayesian algorithm, to optimise a Schottky based solar cell (SBSC) using InGaN as the absorber. The obtained photovoltaic efficiency is close to the conventional structures efficiency while being less complex to elaborate. In addition, the results have shown that the optimised SBSC structure exhibits high fabrication tolerances.
Highlights
The optimisation of thin film solar cells involves many physical and technological parameters such as the active layers’ composition, doping, thickness, optical parameters and density of defects, etc
The global Bayesian algorithm implemented in SoLAr ceLl multivariate OptiMizer (SLALOM) is used in this work to optimise a Schottky based solar cell (SBSC) using indium gallium nitride (InGaN) as the absorber
The InGaN SBSC exhibits an optimal efficiency of approximately 22%, very close to the record efficiency obtained for the well-established thin film solar cells.[10]
Summary
The optimisation of thin film solar cells involves many physical and technological parameters such as the active layers’ composition, doping, thickness, optical parameters and density of defects, etc. One main prerequisite for these methods is to take into account the solar cell parameters’ interdependence. To optimise the solar cell efficiency with respect to all these parameters, the so-called parametric analysis is usually used: only one parameter is varied at a time while the other parameters are kept constant.[1,2] This standard procedure has two main drawbacks. It does not take into account the parameters’ interdependence highlighted previously, and secondly, it does not give the absolute optimal efficiency. The new methodology based on a global Bayesian algorithm addressing these drawbacks is presented
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