Machine-learning techniques have gained popularity in nanophotonics research, being applied to predict optical properties, and inversely design structures. However, one limitation is the cost of acquiring training data, as complex structures require time-consuming simulations. To address this, researchers have explored using transfer learning, where pretrained networks can facilitate convergence with fewer data for related tasks, but application to more difficult tasks is still limited. In this work, a nested transfer learning approach is proposed, training models to predict structures of increasing complexity, with transfer between each model and few data used at each step. This allows modeling thin film stacks with higher optical complexity than previously reported. For the forward model, a bidirectional recurrent neural network is utilized, which excels in modeling sequential inputs. For the inverse model, a convolutional mixture density network is employed. In both cases, a relaxed choice of materials at each layer is introduced, making the approach more versatile. The final nested transfer models display high accuracy in retrieving complex arbitrary spectra and matching idealized spectra for specific application-focused cases, such as selective thermal emitters, while keeping data requirements modest. Our nested transfer learning approach represents a promising avenue for addressing data acquisition challenges.
Great efforts have been made to explore the Fano resonances in two-dimensional transition metal dichalcogenides (TMDs) coupled with plasmonic nanostructures in the visible region. However, the intrinsic losses of metallic materials and the TMD exciton linewidths of at least tens of meV at room temperature (RT) inevitably limit the achievable Q factor of the Fano resonance. Herein, we integrate a monolayer WS2 with single hydrogenated amorphous silicon nanospheres (SiNSs) in water. Pronounced asymmetric Fano resonances with a Q factor up to 104 at the A exciton frequency (2.0 eV) are observed at RT. Fano fitting and modified coupled-mode theory both suggest a decreased A exciton linewidth of ~10 meV as compared to the reported value (~60 meV). This is attributed to the enhanced decay of trion in WS2. Moreover, directional Fano coupling can be achieved by exciting the hybrid from the SiNS or WS2 side, providing more possibilities in device implementation.
Due to the intrinsically weak chirality of biomolecules, discriminating between enantiomers, i.e., chiral molecules of opposite handedness, in low-concentrated solutions by an optical means is one of the unsolved problems in nano-optics. It is even more challenging to separate chiral objects at the nanoscale by optical forces. The key to alleviating the fundamental difficulty of these tasks is to construct an optical field, preferentially in the ultraviolet (UV) region, that carries intense optical chirality with comparable contributions from its electric and magnetic components. However, this requirement has not been met in nanophotonic structures, predominantly because of the lack of magnetic responses in plasmonic materials and the insufficient field enhancement by dielectrics. Innovative designs are highly desired to overcome the limitations from materials. In this work, we systematically investigate the resonance modes in a dielectric metasurface as well as their evolution and interplay as the design variables are engineered. We show that, based on two different mechanisms, 100-fold enhancement of optical chirality can be achieved at near-UV wavelengths with different linewidths. The first one arises from the sharp Fano interference between two distinct magnetic resonances of the unit cell of the metasurface, both of which are enhanced by the coupling across the lattice. The second one originates solely from the magnetic dipole resonance, whereas the chiral hotspots spatially overlap the electric counterparts, forming ideal sites to exert helicity-dependent optical forces on chiral objects at the nanoscale. Our findings pave the way towards practical solutions to the ultimate challenges of chiral optics.
Machine learning (ML) has emerged in recent years as a data-driven approach for photonic inverse design. Despite their impressive performance in finding abstract mappings between the design parameters and optical properties, ML algorithms suffer from a high likelihood of slow converging when there exist multiple designs giving similar optical responses. Here we adopt a deep convolutional mixture density neural network, which models the design as a mixture of Gaussian distributions rather than discrete values, to address the non-uniqueness issue. An example of layered structures consisting of alternating oxides under arbitrary incidence conditions is present to showcase the proof of concept.
Mid-infrared (MIR) spectroscopy is a powerful technique for molecular sensing through identifying the vibrational fingerprints of analyte molecules. However, the sensing efficiency drops dramatically at the nanoscale due to the poor interaction between MIR light and nanometric molecules. Here we exploit the MIR magnetic dipole resonance in a single silicon Mie antenna to demonstrate enhanced molecular sensing. We show that an ultra-sensitive measurement of a sub-10-nm PMMA layer can be achieved by positioning the antennas at the anti-node of a standing wave, which enables light-matter interactions under enhanced resonance conditions. Our results provide a new approach towards high-sensitivity MIR molecular sensing based on a miniaturized photonic platform.
KEYWORDS: Near field optics, Near ultraviolet, Dichroic materials, Dielectrics, Molecules, Nanoparticles, Chemical reactions, Life sciences, Nanostructures, Interfaces
The functionality of biomolecules is largely relevant to their structural chirality, especially the handedness. Discriminating between enantiomers, namely mirror-imaged chiral molecule pairs, and physically separating them are thus vitally important in life sciences. However, completing these tasks by an optical means is very challenging, because the molecular chirality is intrinsically weak. Here we numerically study a design of dielectric metasurfaces for enhancing the near-ultraviolet circular dichroism of chiral molecules and for enantioselective separation of chiral nanoparticles by optical forces. The proposed device can also function as a helicity-preserving meta-mirror. Our findings may pave the way toward practical chiroptical devices.
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