研究目的
To optimize the search for the optimal thickness in solar cells with regards to maximizing short-circuit current density using genetic evolutionary algorithm.
研究成果
The introduction of evolutionary algorithm results in a satisfactorily accurate search method when compared to brute-force. Future works will explore more complex breeding methods and multi-layer optimization.
研究不足
The brute-force method is computationally expensive and redundant. The evolutionary algorithm may result in wrong outputs due to local maxima and minima in the curve.
1:Experimental Design and Method Selection:
Utilized genetic evolutionary algorithm to optimize the search for the optimal thickness in solar cells.
2:Sample Selection and Data Sources:
Solar cell device structure with ZnO and MoOx as charge transport layers.
3:List of Experimental Equipment and Materials:
FDTD software, Lumerical, FDTD solutions.
4:Experimental Procedures and Operational Workflow:
Performed multiple simulations with different number of population, number of generations, mutation probability, number of bits, and selection and crossover methods.
5:Data Analysis Methods:
Compared the accuracy and time consumption of brute-force and evolutionary algorithm methods.
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