Optimal Design of Thin-film Plasmonic Solar Cells using Differential Evolution Optimization Algorithms

TL;DR

This paper presents a differential evolution optimization approach to enhance the optical absorption efficiency of thin-film plasmonic solar cells, achieving over 19.45% improvement in simulated absorption compared to reference cells.

Contribution

It introduces a novel DE-based optimization method for designing plasmonic solar cells, enabling real-time parameter tuning and improved optical performance.

Findings

Optical absorption increased by over 19.45% using the optimized design.

The DE algorithm effectively automates parameter tuning for PSCs.

The method enhances accuracy and reduces time in PSC design process.

Abstract

An approach using a differential evolution (DE) optimization algorithm is proposed to optimize design parameters for improving the optical absorption efficiency of plasmonic solar cells (PSC). This approach is based on formulating the parameters extraction as a search and optimization process in order to maximize the optical absorption in the PSC. Determining the physical parameters of three-dimensional (3-D) PSC is critical for designing and estimating their performance, however, due to the complex design of the PSC, parameters extraction is time and calculation intensive. In this paper, this technique is demonstrated for the case of commercial thin-film hydrogenated amorphous silicon (a-Si:H) solar photovoltaic cells enhanced through patterned silver nano-disk plasmonic structures. The DE optimization of PSC structures was performed to execute a real-time parameter search and optimization. The predicted optical enhancement (OE) in optical absorption in the active layer of the PSC for AM-1.5 solar spectrum was found to be over 19.45% higher compared to the reference cells. The proposed technique offers higher accuracy and automates the tuning of control parameters of PSC in a time-efficient manner.