High optical storage density using three-dimensional hybrid nanostructures based on machine learning

Ding, Yang; Lei, Zhidan; Li, Lijie; Shen, Wei; Li, Hui; Chen, Gui; Song, Yi

doi:10.1016/j.optlaseng.2022.107347

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…The manufacturing noise and measurement noise vary according to the actual measurement equipment and environment. According to the research of D. Yang et al [ 16 ], it is reasonable to set the manufacturing noise and measurement noise at 10%, respectively. Figure 8 a shows the cross-entropy loss corresponding to the 12, 18, and 120 data sets at different polarizations.…”

Section: Analysis and Evaluation Of Readout Informationmentioning

confidence: 99%

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A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography

et al. 2023

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To enable high-density optical storage, better storage media structures, diversified recording methods, and improved accuracy of readout schemes should be considered. In this study, we propose a novel three-dimensional (3D) sloppy nanostructure as the optical storage device, and this nanostructure can be fabricated using the 3D laser direct writing technology. It is a 900 nm high, 1 × 2 µm wide Si slope on a 200 nm SiO2 layer with 200 nm Si3N4 deposited on top to enhance reflectivity. In this study, we propose a reflected spectrum-based method as the readout recording strategy to stabilize information readout more stable. The corresponding reflected spectrum varied when the side wall angle of the slope and the azimuth angle of the nanostructure were tuned. In addition, an artificial neural network was applied to readout the stored information from the reflected spectrum. To simulate the realistic fabrication error and measurement error, a 20% noise level was added to the study. Our findings showed that the readout accuracy was 99.86% for all 120 data sequences when the slope and azimuth angle were varied. We investigated the possibility of a higher storage density to fully demonstrate the storage superiority of this designed structure. Our findings also showed that the readout accuracy can reach its highest level at 97.25% when the storage step of the encoded structure becomes 7.5 times smaller. The study provides the possibility to further explore different nanostructures to achieve high-density optical storage.

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Section: Analysis and Evaluation Of Readout Informationmentioning

confidence: 99%

“…Polarization modulation and readout scheme-dependent (DNA-based) signals require precise patterning and complex readout schemes. Volume product-dependent (holographic memory) is difficult to obtain for full product industrialization solutions [ 16 ]. Hence, more research into developing new storage methods has been carried out.…”

Section: Introductionmentioning

confidence: 99%

A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography

et al. 2023

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“…1 It has been demonstrated that artificial neural networks can be trained for accurate and robust information readout from the optical response of different 2D and 3D geometries of dielectric nanostructures. 1,6,7 Color encoding has the potential to enable fast, parallel and precise data extraction from a single measurement over a large area with many nanostructures; possibly enabling future industrialization of such technologies. However, a major challenge is to increase the number of digital bits of information that can be encoded in a single nanostructure, while remaining capable to retrieve this information from a fast color measurement.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for inverse design of dielectric nanostructures with distinguishable RGB color signatures on dark-field microscopy

Jiménez Jaimes,

Ponomareva,

Wiecha

2024

Machine Learning in Photonics

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“…With the rapid advancement in lithography techniques, the demand for precise control of feature size and spatial morphology for nanopatterning is eminent. [1][2][3][4][5] This is principally because the performance characteristics of nanodevices are highly sensitive to the critical dimensions of nanostructures. Consequently, it is essential to achieve arbitrary modulation of nanoscale feature size with high delity in the lithographic process.…”

Section: Introductionmentioning

confidence: 99%

Spatial modulation of scalable nanostructures by combining maskless plasmonic lithography and grayscale-patterned strategy

Han¹,

Ye²,

Wei³

2023

Nanoscale Adv.

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High optical storage density using three-dimensional hybrid nanostructures based on machine learning

Cited by 5 publications

References 22 publications

A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography

A Reflectivity Enhanced 3D Optical Storage Nanostructure Application Based on Direct Laser Writing Lithography

Deep learning for inverse design of dielectric nanostructures with distinguishable RGB color signatures on dark-field microscopy

Spatial modulation of scalable nanostructures by combining maskless plasmonic lithography and grayscale-patterned strategy

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