Far-Field Wavefront Control of Nonlinear Luminescence in Disordered Gold Metasurfaces

Topological Learning for the Classification of Disorder: An Application to the Design of Metasurfaces

Structural disorder can improve the optical properties of metasurfaces, whether it is emerging from some large-scale fabrication methods or explicitly designed and built lithographically. For example, correlated disorder, induced by a minimum inter-nanostructure distance or by hyperuniformity properties, is particularly beneficial for light extraction. Inspired by topology, we introduce numerical descriptors to provide quantitative measures of disorder with universal properties, suitable to treat both uncorrelated and correlated disorder at all length scales. The accuracy of these topological descriptors is illustrated both theoretically and experimentally by using them to design plasmonic metasurfaces with controlled disorder that we then correlate to the strength of their surface lattice resonances. These descriptors are an example of topological tools that can be used for the fast and accurate design of disordered structures or as aid in improving their fabrication methods.

Random fractal-enabled physical unclonable functions with dynamic AI authentication

A physical unclonable function (PUF) is a foundation of anti-counterfeiting processes due to its inherent uniqueness. However, the self-limitation of conventional graphical/spectral PUFs in materials often makes it difficult to have both high code flexibility and high environmental stability in practice. In this study, we propose a universal, fractal-guided film annealing strategy to realize the random Au network-based PUFs that can be designed on demand in complexity, enabling the tags' intrinsic uniqueness and stability. A dynamic deep learning-based authentication system with an expandable database is built to identify and trace the PUFs, achieving an efficient and reliable authentication with 0% "false positives". Based on the roughening-enabled plasmonic network platform, Raman-based chemical encoding is conceptionally demonstrated, showing the potential for improvements in security. The configurable tags in mass production can serve as competitive PUF carriers for high-level anti-counterfeiting and data encryption.

Infrared metasurface absorber based on silicon-based CMOS process

Metasurface with metal-insulator-metal (MIM) structure has absorption properties for incident light at specific wavelengths. In this paper, we propose an infrared metasurface absorber based on silicon-based complementary metal oxide semiconductor (CMOS) process. By adding the prepared infrared metasurface absorber to the liquid crystal on silicon (LCoS) chip, it is used as the absorbing layer of LCoS configured between the pixel unit and the CMOS driver circuit. The effect of zero-order light caused by the gap between pixels in LCoS spatial light modulator (LCoS-SLM) on the light modulation function of the device is effectively reduced. Experiments show that the LCoS-SLM with infrared metasurface absorption structure can eliminate the zero-order light interference between the pixel gaps to a great extent and improve the modulation efficiency of the device. The proposed LCoS-SLM integrating infrared metasurface absorber structure based on silicon-based CMOS process has the advantages of low-cost and high modulation efficiency, which has high application value in the fields of holographic display, optical computing and optical communication.

Visualization of Directional Beaming of Weakly Localized Raman from a Random Network of Silicon Nanowires

Disordered optical media are an emerging class of materials that can strongly scatter light. These materials are useful to investigate light transport phenomena and for applications in imaging, sensing and energy storage. While coherent light can be generated using such materials, its directional emission is typically hampered by their strong scattering nature. Here, the authors directly image Rayleigh scattering, photoluminescence and weakly localized Raman light from a random network of silicon nanowires via real-space microscopy and Fourier imaging. Direct imaging enables us to gain insight on the light transport mechanisms in the random material, to visualize its weak localization length and to demonstrate out-of-plane beaming of the scattered coherent Raman light. The direct visualization of coherent light beaming in such random networks of silicon nanowires offers novel opportunities for fundamental studies of light propagation in disordered media. It also opens venues for the development of next generation optical devices based on disordered structures, such as sensors, light sources, and optical switches.

Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules

Insights into the fascinating molecular world of biological processes are crucial for understanding diseases, developing diagnostics, and effective therapeutics. These processes are complex as they involve interactions between four major classes of biomolecules, i.e., proteins, nucleic acids, carbohydrates, and lipids, which makes it important to be able to discriminate between all these different biomolecular species. In this work, a deep learning-augmented, chemically-specific nanoplasmonic technique that enables such a feat in a label-free manner to not disrupt native processes is presented. The method uses a highly sensitive multiresonant plasmonic metasurface in a microfluidic device, which enhances infrared absorption across a broadband mid-IR spectrum and in water, despite its strongly overlapping absorption bands. The real-time format of the optofluidic method enables the collection of a vast amount of spectrotemporal data, which allows the construction of a deep neural network to discriminate accurately between all major classes of biomolecules. The capabilities of the new method are demonstrated by monitoring of a multistep bioassay containing sucrose- and nucleotides-loaded liposomes interacting with a small, lipid membrane-perforating peptide. It is envisioned that the presented technology will impact the fields of biology, bioanalytics, and pharmacology from fundamental research and disease diagnostics to drug development.