In response to the growing demand for biomolecular diagnostics, metasurface (MS) platforms based on high-Q resonators have demonstrated their capability to detect analytes with smart data processing and image analysis technologies. However, high-Q resonator meta-atom arrays are highly sensitive to the fabrication process and chemical surface functionalization. Thus, spectrum scanning systems are required to monitor the resonant wavelength changes at every step, from fabrication to practical sensing. In this study, we propose an innovative dielectric resonator-independent MS platform that enables spectrometer-less biomolecule detection using artificial intelligence (AI) at a visible wavelength. Functionalizing the focused vortex MS to capture gold nanoparticle (AuNP)-based sandwich immunoassays causes the resulting vortex beam profiles to be significantly affected by the localized surface plasmon resonance (LSPR) occurring between AuNPs and meta-atoms. The convolutional neural network algorithm was carefully trained to accurately classify the AuNP concentration-dependent focused vortex beam, facilitating the determination of the concentration of the targeted diagnostic biomolecule. Successful in situ identification of various biomolecule concentrations was achieved with over 99 % accuracy, indicating the potential of combining an LSPR-susceptible MS platform and AI for continuously tracking various chemical and biological compounds.
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J. Kim et. al, "Trends in Lightweight Kernel for Many core Based High-Performance Computing", Electronics and Telecommunications Trends. Vol. 32, No. 4, 2017, KOGL Type 4: Source Indication + Commercial Use Prohibition + Change Prohibition
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