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Unlike noble metals, refractory plasmonic materials can maintain resilient and attractive optical properties even at comparatively extreme temperatures and high current densities. One refractory plasmonic material of interest is TiN, which exhibits an extremely high melting temperature of about 3000 K and noble-metal-like optical properties in the visible and near-infrared regime. Using lithographically fabricated TiN nanowires and leveraging their ability to host plasmon modes, we have examined plasmonic photothermal heating and photothermoelectric response whose anisotropy and magnitude depend on the width of the nanowires. The photothermoelectric response is consistent with changes in the Seebeck coefficient where the wire fans out to wider contact pads. Upon electrically biasing the structures, Joule heating of the TiN wires can produce detectable thermal emission within the visible and near-IR range, with emission intensity growing rapidly with increasing bias. This emission is consistent with local temperatures exceeding 2000 K, as expected from a finite element model of the Joule heating. 

Abstract Photonic device development (PDD) has achieved remarkable success in designing and implementing new devices for controlling light across various wavelengths, scales, and applications, including telecommunications, imaging, sensing, and quantum information processing. PDD is an iterative, five-step process that consists of: (i) deriving device behavior from design parameters, (ii) simulating device performance, (iii) finding the optimal candidate designs from simulations, (iv) fabricating the optimal device, and (v) measuring device performance. Classically, all these steps involve Bayesian optimization, material science, control theory, and direct physics-driven numerical methods. However, many of these techniques are computationally intractable, monetarily costly, or difficult to implement at scale. In addition, PDD suffers from large optimization landscapes, uncertainties in structural or optical characterization, and difficulties in implementing robust fabrication processes. However, the advent of machine learning over the past decade has provided novel, data-driven strategies for tackling these challenges, including surrogate estimators for speeding up computations, generative modeling for noisy measurement modeling and data augmentation, reinforcement learning for fabrication, and active learning for experimental physical discovery. In this review, we present a comprehensive perspective on these methods to enable machine-learning-assisted PDD (ML-PDD) for efficient design optimization with powerful generative models, fast simulation and characterization modeling under noisy measurements, and reinforcement learning for fabrication. This review will provide researchers from diverse backgrounds with valuable insights into this emerging topic, fostering interdisciplinary efforts to accelerate the development of complex photonic devices and systems. 

Two-dimensional (2D) transition metal carbides, nitrides and carbonitrides, known as MXenes, are of interest as electrocatalysts. Tungsten-based MXenes are predicted to have low overpotentials in the hydrogen evolution reaction but their synthesis has proven difficult due to the calculated instability of their hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a tungsten-based MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4−y precursor by the selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of tungsten and titanium ordering, the presence of vacancy defects in the metal layers, and the lack of oxygen impurities in the carbon layers for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of tungsten and titanium using computational and experimental characterizations. The tungsten-rich basal plane endows W2TiC2Tx MXene with a high electrocatalytic hydrogen evolution reaction performance (∼144 mV overpotential at 10 mA cm−2). This study reports a tungsten-based MXene synthesized from a covalently bonded non-MAX precursor, adding to the synthetic strategies for 2D materials. 

The integration of solid-state single-photon sources with foundry-compatible photonic platforms is crucial for practical and scalable quantum photonic applications. This study explores aluminum nitride (AlN) as a material with properties highly suitable for integrated on-chip photonics and the ability to host defect-center related single-photon emitters. We have conducted a comprehensive analysis of the creation of single-photon emitters in AlN, utilizing heavy ion irradiation and thermal annealing techniques. Subsequently, we have performed a detailed analysis of their photophysical properties. Guided by theoretical predictions, we assessed the potential of Zirconium (Zr) ions to create optically addressable spin defects and employed Krypton (Kr) ions as an alternative to target lattice defects without inducing chemical doping effects. With a 532 nm excitation wavelength, we found that single-photon emitters induced by ion irradiation were primarily associated with vacancy-type defects in the AlN lattice for both Zr and Kr ions. The density of these emitters increased with ion fluence, and there was an optimal value that resulted in a high density of emitters with low AlN background fluorescence. Under a shorter excitation wavelength of 405 nm, Zr-irradiated AlN exhibited isolated point-like emitters with fluorescence in the spectral range theoretically predicted for spin-defects. However, similar defects emitting in the same spectral range were also observed in AlN irradiated with Kr ions as well as in as-grown AlN with intrinsic defects. This result is supportive of the earlier theoretical predictions, but at the same time highlights the difficulties in identifying the sought-after quantum emitters with interesting properties related to the incorporation of Zr ions into the AlN lattice by fluorescence alone. The results of this study largely contribute to the field of creating quantum emitters in AlN by ion irradiation and direct future studies emphasizing the need for spatially localized Zr implantation and testing for specific spin properties. 

Newly discovered silicon nitride quantum emitters hold great promise for industrial-scale quantum photonic applications. We assess the performance of intrinsic room-temperature SiN single-photon emitters for quantum key distribution, showcasing their exceptional brightness and single-photon purity. 

We demonstrate an industrially scalable fabrication process for the integration of SiN/SiO₂single photon emitters into on-chip nanophotonic structures with sub-diffraction limited placement accuracy.