Unified neural network model for predicting optical responses in gold nanostructures
Year: 2025
Type: article
Abstract: In this paper, we present a deep neural network model capable of simultaneously predicting the optical transmittance spectra of three distinct gold nanostructures: nanodisks, truncated cones, and nanocones. Our model achieved a mean squared error loss as low as 0.0015. By systematically varying geometric parameters, we generated a comprehensive dataset to train the model, which captures complex dependencies between structure and optical response. A key finding is the redshift in resonance dip associated with increasing sidewall tilt. We further validated the model by comparing its predictions with experimental results, showing excellent agreement. This work demonstrates a powerful, data-driven approach for the rapid design and optimization of plasmonic nanostructures, opening new possibilities for nanophotonic applications.
Source: Journal of Applied Physics
DOI: 10.1063/5.0249206
Authors: Xiaobo Wang, Yu Cao, Feng Feng, Ziyuan Wang, Yuchen Cao
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,500
OpenAlex ID: W4407337753