Design of highly perceptible dual-resonance all-dielectric metasurface colorimetric sensor via deep neural networks

• Published in: Scientific reports Vol. 12; no. 1; pp. 8512 - 10
• Main Authors: Son, Hyunwoo, Kim, Sun-Je, Hong, Jongwoo, Sung, Jangwoon, Lee, Byoungho
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/624; 639/925; Colorimetry; Design; Environmental changes; Humanities and Social Sciences; multidisciplinary; Neural networks; Optimization techniques; Resonance; Science; Science (multidisciplinary); Sensors
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-12592-9

Colorimetric sensing, which provides effective detection of bio-molecular signals with one’s naked eye, is an exceptionally promising sensing technique in that it enables convenient detection and simplification of entire sensing system. Though colorimetric sensors based on all-dielectric nanostructures have potential to exhibit distinct color variations enabling manageable detection due to their trivial intrinsic loss, there is crucial limitation that the sensitivity to environmental changes lags behind their plasmonic counterparts because of relatively small region of near field-analyte interaction of the dielectric Mie-type resonator. To overcome this challenge, we proposed all-dielectric metasurface colorimetric sensor which exhibits dual-resonance in the visible region. Thereafter, we confirmed with simulation that, in the elaborately designed dual-Lorentzian-type spectra, highly perceptible variations of structural color were manifested even in minute change of peripheral refractive index. In addition to verifying physical effectiveness of the superior colorimetric sensing performance appearing in the dual-resonance type sensor, by combining advanced optimization technique utilizing deep neural networks, we attempted to maximize sensing performance while obtaining dramatic improvement of design efficiency. Through well-trained deep neural network that accurately simulates the input target spectrum, we numerically verified that designed colorimetric sensor shows a remarkable sensing resolution distinguishable up to change of refractive index of 0.0086.