Surface and interface engineering has become a decisive factor in determining the performance and reliability of integrated photonic sensors. As photonic device architectures advance and geometric optimization strategies approach their fundamental performance limits, the nanoscale interface region where confined optical modes interact with the surrounding environment progressively becomes the dominant factor governing sensitivity, noise characteristics, and long-term operational stability. This review critically examines recent advances in these strategies applied to integrated photonic sensing platforms, including waveguide, interferometric, and resonant architectures. Emphasis is placed on how functional layers, nanomaterials, and hybrid interfaces modify light–matter interactions, while simultaneously introducing optical loss, spectral distortion, and stability constraints. Beyond summarizing reported sensitivity enhancements, this review analyzes performance benchmarking methodologies and highlights the limitations of conventional metrics such as bulk sensitivity and nominal limit of detection. Normalized figures of merit are discussed as essential tools for isolating genuine interface contributions across diverse platforms. Experimentally documented trade-offs between enhanced surface interaction, optical degradation, and temporal drift are examined in detail, alongside challenges related to reproducibility, wafer-scale variability, and long-term interface stability. By synthesizing insights from photonics, surface chemistry, and materials science, this review outlines key open questions and identifies design principles necessary for translating surface-engineered photonic sensors from laboratory demonstrations to robust and scalable sensing technologies.
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Nikolay L. Kazanskiy
Kurchatov Institute
Dmitry V. Nesterenko
Kurchatov Institute
Svetlana N. Khonina
Kurchatov Institute
Micromachines
Kurchatov Institute
Samara National Research University
Image Processing Systems Institute
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Kazanskiy et al. (Sat,) studied this question.
synapsesocial.com/papers/69fa8eac04f884e66b530fde — DOI: https://doi.org/10.3390/mi17050522