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Register a new userA Hybrid Strategy for the Discovery and Design of Photonic Nanostructures
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Designing complex physical systems, including photonic structures, is typically a tedious trial-and-error process that requires extensive simulations with iterative sweeps in multi-dimensional parameter space. To circumvent this conventional approach and substantially expedite the discovery and development of photonic structures, here we develop a framework leveraging both a deep generative model and a modified evolution strategy to automate the inverse design of engineered nanophotonic materials. The capacity of the proposed methodology is tested through the application to a case study, where metasurfaces in either continuous or discrete topologies are generated in response to customer-defined spectra at the input. Through a variational autoencoder, all potential patterns of unit nanostructures are encoded into a continuous latent space. An evolution strategy is applied to vectors in the latent space to identify an optimized vector whose nanostructure pattern fulfills the design objective. The evaluation shows that over 95% accuracy can be achieved for all the unit patterns of the nanostructure tested. Our scheme requires no prior knowledge of the geometry of the nanostructure, and, in principle, allows joint optimization of the dimensional parameters. As such, our work represents an efficient, on-demand, and automated approach for the inverse design of photonic structures with subwavelength features.
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Resonance is instrumental in modern optics and photonics for novel phenomena such as cavity quantum electrodynamics and electric-field-induced transparency. While one can use numerical simulations to sweep geometric and material parameters of optical structures, these simulations usually require considerably long calculation time (spanning from several hours to several weeks) and substantial computational resources. Such requirements significantly limit their applicability in understanding and inverse designing structures with desired resonance performances. Recently, the introduction of artificial intelligence allows for faster predictions of resonance with less demanding computational requirements. However, current end-to-end deep learning approaches generally fail to predict resonances with high quality-factors (Q-factor). Here, we introduce a universal deep learning strategy that can predict ultra-high Q-factor resonances by decomposing spectra with an adaptive data acquisition (ADA) method while incorporating resonance information. We exploit bound states in the continuum (BICs) with an infinite Q-factor to testify this resonance-informed deep learning (RIDL) strategy. The trained RIDL strategy achieves high-accuracy prediction of reflection spectra and photonic band structures while using a considerably small training dataset. We further develop an inverse design algorithm based on the RIDL strategy for a symmetry-protected BIC on a suspended silicon nitride photonic crystal (PhC) slab. The predicted and measured angle-resolved band structures show minimum differences. We expect the RIDL strategy to apply to many other physical phenomena which exhibit Gaussian, Lorentzian, and Fano resonances.
We present a quantization scheme for optical systems with absorptive losses, based on an expansion in the complete set of scattering solutions to Maxwells equations. The natural emergence of both absorptive loss and fluctuations without introducing a thermal bath is demonstrated. Our model predicts mechanisms of absorption induced squeezing and dispersion mediated photon conversion.
This article offers an extensive survey of results obtained using hybrid photonic crystal fibers (PCFs) which constitute one of the most active research fields in contemporary fiber optics. The ability to integrate novel and functional materials in solid- and hollow-core PCFs through various post-processing methods has enabled new directions towards understanding fundamental linear and nonlinear phenomena as well as novel application aspects, within the fields of optoelectronics, material and laser science, remote sensing and spectroscopy. Here the recent progress in the field of hybrid PCFs is reviewed from scientific and technological perspectives, focusing on how different fluids, solids and gases can significantly extend the functionality of PCFs. In the first part of this review we discuss the most important efforts by research groups around the globe to develop tunable linear and nonlinear fiber-optic devices using PCFs infiltrated with various liquids, glasses, semiconductors and metals. The second part is concentrated on the most recent and state-of-the-art advances in the field of gas-filled hollow-core PCFs. Extreme ultrafast gas-based nonlinear optics towards light generation in the extreme wavelength regions of vacuum ultraviolet (VUV), pulse propagation and compression dynamics in both atomic and molecular gases, and novel soliton - plasma interactions are reviewed. A discussion of future prospects and directions is also included.
The long dreamed quantum internet would consist of a network of quantum nodes (solid-state or atomic systems) linked by flying qubits, naturally based on photons, travelling over long distances at the speed of light, with negligible decoherence. A key component is a light source, able to provide single or entangled photon pairs. Among the different platforms, semiconductor quantum dots are very attractive, as they can be integrated with other photonic and electronic components in miniaturized chips. In the early 1990s two approaches were developed to synthetize self-assembled epitaxial semiconductor quantum dots (QDs), or artificial atoms, namely the Stranski-Krastanov (SK) and the droplet epitaxy (DE) method. Because of its robustness and simplicity, the SK method became the workhorse to achieve several breakthroughs in both fundamental and technological areas. The need for specific emission wavelengths or structural and optical properties has nevertheless motivated further research on the DE method and its more recent development, the local-droplet-etching (LDE), as complementary routes to obtain high quality semiconductor nanostructures. The recent reports on the generation of highly entangled photon pairs, combined with good photon indistinguishability, suggest that DE and LDE QDs may complement (and sometime even outperform) conventional SK InGaAs QDs as quantum emitters. We present here a critical survey of the state of the art of DE and LDE, highlighting the advantages and weaknesses, the obtained achievements and the still open challenges, in view of applications in quantum communication and technology.
Deterministically integrating single solid-state quantum emitters with photonic nanostructures serves as a key enabling resource in the context of photonic quantum technology. Due to the random spatial location of many widely-used solid-state quantum emitters, a number of positoning approaches for locating the quantum emitters before nanofabrication have been explored in the last decade. Here, we review the working principles of several nanoscale positioning methods and the most recent progress in this field, covering techniques including atomic force microscopy, scanning electron microscopy, confocal microscopy with textit{in situ} lithography, and wide-field fluorescence imaging. A selection of representative device demonstrations with high-performance is presented, including high-quality single-photon sources, bright entangled-photon pairs, strongly-coupled cavity QED systems, and other emerging applications. The challenges in applying positioning techniques to different material systems and opportunities for using these approaches for realizing large-scale quantum photonic devices are discussed.
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