Artificial neural networks assisting the design of a dual-mode photonic crystal nanobeam cavity for simultaneous sensing of the refractive index and temperature

Artificial neural network assisted the design of subwavelength-grating waveguides for nanoparticles optical trapping

In this work, we propose artificial neural networks (ANNs) to predict the optical forces on particles with a radius of 50 nm and inverse-design the subwavelength-grating (SWG) waveguides structure for trapping. The SWG waveguides are applied to particle trapping due to their superior bulk sensitivity and surface sensitivity, as well as longer working distance than conventional nanophotonic waveguides. To reduce the time consumption of the design, we train ANNs to predict the trapping forces and to inverse-design the geometric structure of SWG waveguides, and the low mean square errors (MSE) of the networks achieve 2.8 × 10-4. Based on the well-trained forward prediction and inverse-design network, an SWG waveguide with significant trapping performance is designed. The trapping forces in the y-direction achieve-40.39 pN when the center of the particle is placed 100 nm away from the side wall of the silicon segment, and the negative sign of the optical forces indicates the direction of the forces. The maximum trapping potential achieved to 838.16 kBT in the y-direction. The trapping performance in the x and z directions is also quite superior, and the neural network model has been further applied to design SWGs with a high trapping performance. The present work is of significance for further research on the application of artificial neural networks in other optical devices designed for particle trapping.

Compact dual-parameter sensor design based on a photonic crystal nanobeam cavity with chirped slotted annular holes

A compact photonic crystal nanobeam cavity with a 20µm×0.8µm footprint supporting simultaneous air and dielectric resonant modes is proposed for dual-parameter sensing of refractive index and temperature. The structure consists of a row of chirped annular holes and an air-slot etched in an asymmetrical silicon slab. By tapering the lattice period and hole radius, the bands for air and dielectric modes shift in opposite directions, enabling confinement in a single cavity. Numerical simulations determine refractive index sensitivities of 173.59 nm/RIU for the air mode and 286.82 nm/RIU for the dielectric mode. Temperature sensitivities are 69.6 pm/°C and 78.7 pm/°C for the two modes, respectively. The structure demonstrates strong resistance to external interference with refractive index and temperature disturbance resistance coefficients of 2.3×10-5 and 0.07. The high sensitivities in an ultracompact footprint with resistance to crosstalk make this dual-mode nanocavity promising for on-chip biochemical sensing applications.

Inverse design of a photonic moiré lattice waveguide towards improved slow light performances

Slow light waveguides in photonic crystals are engineered using a conventional method or a deep learning (DL) method, which is data-intensive and suffers from data inconsistency, and both methods result in overlong computation time with low efficiency. In this paper, we overcome these problems by inversely optimizing the dispersion band of a photonic moiré lattice waveguide using automatic differentiation (AD). The AD framework allows the creation of a definite target band to which a selected band is optimized, and a mean square error (MSE) as an objective function between the selected and the target bands is used to efficiently compute gradients using the autograd backend of the AD library. Using a limited-memory Broyden-Fletcher-Goldfarb-Shanno minimizer algorithm, the optimization converges to the target band, with the lowest MSE value of 9.844×10^-7, and a waveguide that produces the exact target band is obtained. The optimized structure supports a slow light mode with a group index of 35.3, a bandwidth of 110 nm, and a normalized-delay-bandwidth-product of 0.805, which is a 140.9% and 178.9% significant improvement if compared to conventional and DL optimization methods, respectively. The waveguide could be utilized in slow light devices for buffering.