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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. 

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. 

Layered metal-halide perovskites, or two-dimensional perovskites, can be synthesized in solution, and their optical and electronic properties can be tuned by changing their composition. We report a molecular templating method that restricted crystal growth along all crystallographic directions except for [110] and promoted one-dimensional growth. Our approach is widely applicable to synthesize a range of high-quality layered perovskite nanowires with large aspect ratios and tunable organic-inorganic chemical compositions. These nanowires form exceptionally well-defined and flexible cavities that exhibited a wide range of unusual optical properties beyond those of conventional perovskite nanowires. We observed anisotropic emission polarization, low-loss waveguiding (below 3 decibels per millimeter), and efficient low-threshold light amplification (below 20 microjoules per square centimeter). 

Two-dimensional transition metal carbides, nitrides, and carbonitrides, known as MXenes, hold potential in electrocatalytic applications. Tungsten (W) based-MXenes are of particular interest as they are predicted to have low overpotentials in hydrogen evolution reaction (HER). However, incorporating W into the MXene structure has proven difficult due to the calculated instability of its hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a W-containing MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4-y precursor by selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of W and Ti ordering and the presence of vacancy defects for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of W and Ti using density functional theory, Rietveld refinement, and electron microscopy methods. Additionally, the W-rich basal plane endows W2TiC2Tx MXene with a remarkable HER overpotential (~144 mV at 10 mA/cm2). This study adds a tungsten-containing MXene made from a covalently bonded non-MAX phase opening more ways to synthesize novel 2D materials. 

We report on the generation of single-photon emitters in aluminum nitride films through Zr-ion implantation, which was predicted to form optically addressable spin defects. We studied implantation conditions, post-implantation procedures, and properties of resulting emitters.