Using a Neural Network to Improve the Optical Absorption in Halide Perovskite Layers Containing Core-Shells Silver...

Area of Science:

• Nanophotonics
• Materials Science
• Renewable Energy

Background:

• Core-shell metallic nanoparticles exhibit dual plasmon resonances in visible and infrared regions.
• Thin-film solar cells require efficient light absorption across broad spectral ranges for improved performance.

Purpose of the Study:

• To enhance optical absorption in Halide Perovskite (CH₃NH₃PbI₃) thin-film solar cells using silver core-shell nanoparticles.
• To leverage neural networks for optimizing nanoparticle configurations for maximum light absorption.

Main Methods:

• Finite Difference Time Domain (FDTD) simulations were conducted for various silver core-shell configurations within perovskite thin films.
• A neural network was trained using FDTD simulation data to identify optimal core-shell designs.
• Optical absorption was analyzed across visible and infrared wavelengths.

Main Results:

• Identified specific silver core-shell configurations that simultaneously enhance optical absorption in both visible and infrared spectra.
• Demonstrated the effectiveness of neural networks in navigating the vast design space of nanoparticle configurations.
• Achieved significant improvements in optical absorption through optimized core-shell nanoparticle integration.

Conclusions:

• Core-shell nanoparticles offer a viable strategy for enhancing solar cell efficiency by broadening spectral absorption.
• Neural networks are powerful tools for optimizing complex nanophotonic systems for energy applications.
• This approach holds promise for advancing the performance of next-generation thin-film solar cells.