arXiv subjects

Mikko Huttunen

Publications and source records attributed to Mikko Huttunen.

3 recordsLinked to original sources

arXiv2025

Photonic Doping of Epsilon-Near-Zero Bragg Microcavities

Epsilon-near-zero (ENZ) photonics provides a powerful route to extreme dispersion engineering, strong field confinement, and unconventional wave phenomena. A closely related concept is \textit{photonic doping}, where subwavelength nonmagnetic dielectric materials embedded in ENZ media enable exotic responses such as perfect-magnetic-conductor behavior and simultaneous epsilon- and mu-near-zero states. However, photonic doping has remained limited to microwave and far-infrared regimes due to the intrinsic losses of optical ENZ materials. Here, photonic doping is demonstrated at optical frequencies by embedding a periodic array of dielectric Mie resonators into an ultralow-loss, all-dielectric ENZ platform based on near-cutoff Bragg microcavities. The resulting structures support spectrally isolated, quasi-singular coupled Bragg--Mie resonances spanning electric and magnetic multipolar orders and their overtones. These modes exhibit effective near-zero-index dispersion with fields confined either within or between the nanoparticles. A representative $14\,μ\mathrm{m}$-scale doped structure exhibits quality factors approaching $10^{4}$ and magnetic-dipole Purcell enhancements exceeding $5\times10^{3}$ in the near-infrared. The demonstrated platform elevates the photonic doping from a microwave-only concept to a fully optical, low-loss, and multipole-resolved platform, enabling ultra-narrowband Mie-like resonances, enhanced magnetic-light interactions, and new opportunities in multipolar-selective spectroscopy and lasing, low-threshold nonlinear optics and efficient single-photon emission.

physics.optics↗

arXiv2025

Purcell Effect in Epsilon-Near-Zero Microcavities

Epsilon-near-zero (ENZ) photonics presents a powerful platform for integrated photonic systems, enabling a range of novel and extraordinary functionalities. However, the practical implementation of ENZ-based systems is often constrained by high material losses and severe impedance mismatch, limiting the efficient interaction of light with ENZ media. To overcome these challenges, we introduce all-dielectric Bragg reflection microcavities operating at their cutoff frequency as a high-figure-of-merit ENZ resonant platform, providing an ultra-low-loss alternative for studying emission processes in ENZ media. While Bragg cavities are well-established, their potential as ENZ resonant microcavities remains largely unexplored. We investigate the Purcell effect and quality factor in these structures, comparing their performance with those of the perfect-electric-conductor and metallic counterparts. Through analytical derivations based on Fermi's golden rule and field quantization in lossless dispersive media, we establish scaling laws that distinguish these ENZ cavities from conventional resonators. Frequency-domain simulations validate our findings, demonstrating that in all-dielectric ENZ Bragg-reflection microcavities, the Purcell and quality factors scale as $L/λ_0$ and $(L/λ_0)^3$, respectively, where $L$ is the cavity length and $λ_0$ is the resonance wavelength. Our results offer key insights into the design of ENZ-based photonic systems, paving the way for enhanced light-matter interactions in nonlinear optics and quantum photonics.

physics.optics↗

arXiv2022

On the Automated Detection of Corneal Edema with Second Harmonic Generation Microscopy and Deep Learning

When the cornea becomes hydrated above its physiologic level it begins to significantly scatter light, loosing transparency and thus impairing eyesight. This condition, known as corneal edema, can be associated with different causes, such as corneal scarring, corneal infection, corneal inflammation, and others, making it difficult to diagnose and quantify. Previous works have shown that Second Harmonic Generation Microscopy (SHG) represents a valuable non-linear optical imaging tool to non-invasively identify and monitor changes in the collagen architecture of the cornea, potentially playing a pivotal role in future in-vivo cornea diagnostic methods. However, the interpretation of SHG data can pose significant problems when transferring such approaches to clinical settings, given the low availability of public data sets, and training resources. In this work we explore the use of three Deep Learning models, the highly popular InceptionV3 and ResNet50, alongside FLIMBA, a custom developed architecture, requiring no pre-training, to automatically detect corneal edema in SHG images of porcine cornea. We discuss and evaluate data augmentation strategies tuned to the specifics of the herein addressed application and observe that Deep Learning models building on different architectures provide complementary results. Importantly, we observe that the combined use of such complementary models boosts the overall classification performance in the case of differentiating edematous and healthy corneal tissues, up to an AU-ROC=0.98. These results have potential to be extrapolated to other diagnostics scenarios, such as differentiation of corneal edema in different stages, automated extraction of hydration level of cornea, or automated identification of corneal edema causes, and thus pave the way for novel methods for cornea diagnostics with Deep-Learning assisted non-linear optical imaging.

physics.med-ph↗