The article provides information about the image on the cover of Advanced Photonics, Volume 4, Issue 6.
Naomi Halas, professor of electrical and computer engineering at Rice University and winner of the prestigious 2022 Eni Energy Transition Award, discusses her vision for the future of plasmonics and nanophotonics, in terms of fundamentals and applications, in conversation with Jia Zhu, professor in the College of Engineering and Applied Science at Nanjing University and associate editor for Advanced Photonics.
Kerr frequency combs have been attracting significant interest due to their rich physics and broad applications in metrology, microwave photonics, and telecommunications. In this review, we first introduce the fundamental physics, master equations, simulation methods, and dynamic process of Kerr frequency combs. We then analyze the most promising material platform for realizing Kerr frequency combs—silicon nitride on insulator (SNOI) in comparison with other material platforms. Moreover, we discuss the fabrication methods, process optimization as well as tuning and measurement schemes of SNOI-based Kerr frequency combs. Furthermore, we highlight several emerging applications of Kerr frequency combs in metrology, including spectroscopy, ranging, and timing. Finally, we summarize this review and envision the future development of chip-scale Kerr frequency combs from the viewpoint of theory, material platforms, and tuning methods.
The explosion in the amount of information that is being processed is prompting the need for new computing systems beyond existing electronic computers. Photonic computing is emerging as an attractive alternative due to performing calculations at the speed of light, the change for massive parallelism, and also extremely low energy consumption. We review the physical implementation of basic optical calculations, such as differentiation and integration, using metamaterials, and introduce the realization of all-optical artificial neural networks. We start with concise introductions of the mathematical principles behind such optical computation methods and present the advantages, current problems that need to be overcome, and the potential future directions in the field. We expect that our review will be useful for both novice and experienced researchers in the field of all-optical computing platforms using metamaterials.
We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our PINNs can be generalized for any forward and inverse scattering problem.
Direct laser writing (DLW) enables arbitrary three-dimensional nanofabrication. However, the diffraction limit poses a major obstacle for realizing nanometer-scale features. Furthermore, it is challenging to improve the fabrication efficiency using the currently prevalent single-focal-spot systems, which cannot perform high-throughput lithography. To overcome these challenges, a parallel peripheral-photoinhibition lithography system with a sub-40-nm two-dimensional feature size and a sub-20-nm suspended line width was developed in our study, based on two-photon polymerization DLW. The lithography efficiency of the developed system is twice that of conventional systems for both uniform and complex structures. The proposed system facilitates the realization of portable DLW with a higher resolution and throughput.
In the quest to realize a scalable quantum network, semiconductor quantum dots (QDs) offer distinct advantages, including high single-photon efficiency and indistinguishability, high repetition rate (tens of gigahertz with Purcell enhancement), interconnectivity with spin qubits, and a scalable on-chip platform. However, in the past two decades, the visibility of quantum interference between independent QDs rarely went beyond the classical limit of 50%, and the distances were limited from a few meters to kilometers. Here, we report quantum interference between two single photons from independent QDs separated by a 302 km optical fiber. The single photons are generated from resonantly driven single QDs deterministically coupled to microcavities. Quantum frequency conversions are used to eliminate the QD inhomogeneity and shift the emission wavelength to the telecommunication band. The observed interference visibility is 0.67 ± 0.02 (0.93 ± 0.04) without (with) temporal filtering. Feasible improvements can further extend the distance to ∼600 km. Our work represents a key step to long-distance solid-state quantum networks.
Structured light fields embody strong spatial variations of polarization, phase, and amplitude. Understanding, characterization, and exploitation of such fields can be achieved through their topological properties. Three-dimensional (3D) topological solitons, such as hopfions, are 3D localized continuous field configurations with nontrivial particle-like structures that exhibit a host of important topologically protected properties. Here, we propose and demonstrate photonic counterparts of hopfions with exact characteristics of Hopf fibration, Hopf index, and Hopf mapping from real-space vector beams to homotopic hyperspheres representing polarization states. We experimentally generate photonic hopfions with on-demand high-order Hopf indices and independently controlled topological textures, including Néel-, Bloch-, and antiskyrmionic types. We also demonstrate a robust free-space transport of photonic hopfions, thus showing the potential of hopfions for developing optical topological informatics and communications.
Perovskite light-emitting diodes (PeLEDs) are considered as promising candidates for next-generation solution-processed full-color displays. However, the external quantum efficiencies (EQEs) and operational stabilities of deep-blue (n values >3 is hampered completely, so that phase-pure 2D-RPP films with bandgaps suitable for deep-blue PeLEDs can be obtained successfully. The uniquely developed rapid crystallization method also enables formation of randomly oriented 2D-RPP crystals, thereby improving the transfer and transport kinetics of the charge carriers. Thus, high-performance deep-blue PeLEDs emitting at 437 nm with a peak EQE of 0.63% are successfully demonstrated. The color coordinates are confirmed to be (0.165, 0.044), which match well with the Rec.2020 standard blue gamut and have excellent spectral stability.
Multiphoton resonant excitation and frustrated tunneling ionization, manifesting the photonic and optical nature of the driving light via direct excitation and electron recapture, respectively, are complementary mechanisms to access Rydberg state excitation (RSE) of atoms and molecules in an intense laser field. However, clear identification and manipulation of their individual contributions in the light-induced RSE process remain experimentally challenging. Here, we bridge this gap by exploring the dissociative and nondissociative RSE of H2 molecules using bicircular two-color laser pulses. Depending on the relative field strength and polarization helicity of the two colors, the RSE probability can be boosted by more than one order of magnitude by exploiting the laser waveform-dependent field effect. The role of the photon effect is readily strengthened with increasing relative strength of the second-harmonic field of the two colors regardless of the polarization helicity. As compared to the nondissociative RSE forming H2 * , the field effect in producing the dissociative RSE channel of ( H + , H * ) is moderately suppressed, which is primarily accessed via a three-step sequential process separated by molecular bond stretching. Our work paves the way toward a comprehensive understanding of the interplay of the underlying field and photon effects in the strong-field RSE process, as well as facilitating the generation of Rydberg states optimized with tailored characteristics.
Large-scale linear operations are the cornerstone for performing complex computational tasks. Using optical computing to perform linear transformations offers potential advantages in terms of speed, parallelism, and scalability. Previously, the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination. We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected, complex-valued linear transformations between an input and output field of view, each with Ni and No pixels, respectively. This broadband diffractive processor is composed of Nw wavelength channels, each of which is uniquely assigned to a distinct target transformation; a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths, either simultaneously or sequentially (wavelength scanning). We demonstrate that such a broadband diffractive network, regardless of its material dispersion, can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons (N) in its design is ≥2NwNiNo. We further report that the spectral multiplexing capability can be increased by increasing N; our numerical analyses confirm these conclusions for Nw > 180 and indicate that it can further increase to Nw ∼ 2000, depending on the upper bound of the approximation error. Massively parallel, wavelength-multiplexed diffractive networks will be useful for designing high-throughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.