Ivan A. Kazakov | Searcharxiv

arXiv2026

Moth's eye-inspired perfectly vertical subwavelength grating coupler for silicon photonics

We propose a novel bio-inspired design principle for the perfectly vertical grating coupler. The main idea of our design is to introduce anisotropy to the grating stripe to direct the light to one side of the grating. This grating design is easy to manufacture, only requiring a single etching step, and it is designed to efficiently couple vertically incident light. This makes it a good candidate for heterogeneous integration of light sources, especially VCSELs, on chip for applications in classical and quantum communications, LIDARs, sensing systems, and others. The grating coupler was designed for the SOI material platform with a central wavelength of 1550 nm. We obtained the efficiency of in-coupling from the SMF-28 fiber of 41% at vertical incidence and unidirectionality of over 10 dB, with a bandwidth of 50 nm at a 1 dB level in simulation. Experimental measurements confirmed unidirectionality, with observed unidirectionality of 12.80+-0.02 dB and a single-coupler insertion loss of 8.35+-0.02 dB around 1528 nm.

physics.optics↗

arXiv2025

Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator

We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regression model. The analytical method achieves a root mean square error (RMSE) of 7.11 pm within the calibrated range, while the ML approach based on exponential regression achieves 3.17 pm. Moreover, the ML model demonstrates generalization across an extended 2.9 nm wavelength span, maintaining sub-5 pm accuracy without re-fitting. Residual and error distribution analyses further illustrate the trade-offs between the two approaches. ML-based calibration provides a robust, data-driven alternative to analytical methods, delivering enhanced accuracy for non-ideal channel responses, reduced manual calibration effort, and improved scalability across diverse FBG sensor configurations.

physics.optics↗