Risk factors associated with fall-related occupational injuries include older age and being at a construction area during regular working hours. Falls among occupational injuries are more severe than other injuries and result in longer work loss.
Metasurface color filters (MCFs) have attracted considerable attention thanks to their compactness and functionality as a candidate of an optical element in a miniaturized image sensor. However, conventional dielectric and plasmonic MCFs that have focused on color purity and efficiency cannot avoid reflection in principle, which degrades image quality by optical flare. Here, we introduce absorptive-type MCFs through truncated-cone hyperbolic metamaterial absorbers. By applying a particle swarm optimization method to design multiple parameters simultaneously, the proposed MCF is theoretically and numerically demonstrated in perceptive color on CIELAB and CIEDE2000 with suppressed-reflection. Then, a color filter array is numerically proven in 255 nm of sub-pixel pitch.
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.
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