Year: 2024
Type: article
Abstract: Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.
Source: Nano Letters
Institutions: University of California San Diego, Northeastern University
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Awards: Equipment: MRI: Track # 2 Development of a high-speed super-resolution stimulated Raman scattering (SRS) microscope, CDS&E: Elucidating and Controlling the Spectral, Spatial and Temporal Responses of Plasmonic Nanostructures based on a Data-Driven Approach, G5145407817, G6724960469, G6885780937, Chiroptical Sensing and Sorting by Structured Materials and Structured Light, Non-Hermitian and Topological Plasmonic Devices for Light Manipulation at the Nanoscale, G7831880655
OpenAlex ID: W4402274737