Hasil untuk "Applied optics. Photonics"

F. Bonaccorso, Z. Sun, T. Hasan et al.

Graphene has great potential in photonics and optoelectronics. I will review the state of the art in this emerging field of research, focussing on flexible and transparent conductors, photoluminescence, photodetectors, non-linear optics, and ultrafast lasers.

W. Barnes, A. Dereux, T. Ebbesen

M. Guidry, D. Lukin, K. Yang et al.

Soliton microcombs—phase-locked microcavity frequency combs—have become the foundation of several classical technologies in integrated photonics, including spectroscopy, LiDAR and optical computing. Despite the predicted multimode entanglement across the comb, experimental study of the quantum optics of the soliton microcomb has been elusive. In this work we use second-order photon correlations to study the underlying quantum processes of soliton microcombs in an integrated silicon carbide microresonator. We show that a stable temporal lattice of solitons can isolate a multimode below-threshold Gaussian state from any admixture of coherent light, and predict that all-to-all entanglement can be realized for the state. Our work opens a pathway toward a soliton-based multimode quantum resource. The quantum aspect of soliton microcomb from an integrated silicon carbide microresonator is studied in several regimes — below threshold, above threshold and in the soliton regime — using a single-photon optical spectrum analyser for second-order photon correlation measurement.

J. Krakofsky, Raktim Sarma, Igal Brener et al.

Abstract Nonlinear intersubband polaritonic metasurfaces produce some of the strongest second- and third-order nonlinear optical responses reported for condensed matter systems at infrared frequencies. These metasurfaces are fabricated as two-dimensional arrays of nanoresonators from multi-quantum-well semiconductor heterostructures, designed to produce strong nonlinear responses associated with intersubband transitions. By optimally coupling the optical modes of the nanoresonators to vertically polarized intersubband transitions in semiconductor heterostructures, one can boost the nonlinear response associated with intersubband transitions, make intersubband transitions interact with free-space radiation at normal incidence, and hence produce optically thin flat nonlinear optical elements compatible with free-space optical setups. As a result of the strong nonlinear response in these systems, significant nonlinear conversion efficiencies (>0.1 %) can be attained in deeply subwavelength optical films using modest pumping intensities of only 10–100 kW/cm2. Subwavelength metasurface thickness relaxes phase-matching constraints limiting the operation of bulk nonlinear crystals. Furthermore, the amplitude and phase of the nonlinear optical response in intersubband polaritonic metasurfaces can be tailored for a specific pump wavelength and a nonlinear process of interest through the co-optimization of quantum engineering of electron states in semiconductor heterostructures and photonic engineering of the metasurface nanoresonators design. Additionally, an applied voltage can dynamically control the amplitude and phase of the nonlinear optical response at a nanoresonator level. Here, we review the current state of the art in this rapidly expanding field, focusing on nonlinear processes supporting second-harmonic generation, saturable absorption, and optical power limiting.

Hongjian Zhou, Ziang Yin, Jiaqi Gu

Photonics is becoming a cornerstone technology for high-performance AI systems and scientific computing, offering unparalleled speed, parallelism, and energy efficiency. Despite this promise, the design and deployment of electronic-photonic AI systems remain highly challenging due to a steep learning curve across multiple layers, spanning device physics, circuit design, system architecture, and AI algorithms. The absence of a mature electronic-photonic design automation (EPDA) toolchain leads to long, inefficient design cycles and limits cross-disciplinary innovation and co-evolution. In this work, we present a cross-layer co-design and automation framework aimed at democratizing photonic AI system development. We begin by introducing our architecture designs for scalable photonic edge AI and Transformer inference, followed by SimPhony, an open-source modeling tool for rapid EPIC AI system evaluation and design-space exploration. We then highlight advances in AI-enabled photonic design automation, including physical AI-based Maxwell solvers, a fabrication-aware inverse design framework, and a scalable inverse training algorithm for meta-optical neural networks, enabling a scalable EPDA stack for next-generation electronic-photonic AI systems.

P. Z. Li, W. Zhang, Z. F. Feng et al.

Transition-edge sensors (TESs) are among the most promising technologies for single-photon detection, offering high quantum efficiency, low dark count rates, and energy-resolving capabilities. However, optimal TES performance typically requires ultra-low temperatures below 100 mK, necessitating bulky dilution refrigerators or adiabatic demagnetization refrigerators, which limits their practical deployment. In this work, we demonstrate a titanium-based TES optimized for 1064 nm photon detection that achieves high performance at an elevated bath temperature of 220 mK—approximately 90% of its superconducting transition temperature, making it compatible with more compact sorption refrigerators. Through the integration of an optical cavity, a detailed complex impedance and noise characterization, and a systematic investigation of dark count sources, the detector achieves a system detection efficiency exceeding 97%, an energy resolution of 240 meV, and an intrinsic dark count rate as low as 2 × 10−4 cps. These findings significantly expand the applicability of TES single-photon detectors to practical photon-counting systems, such as quantum information and infrared astronomy.

J. Middendorf, A. Kelm, S. Frintrop

The paper proposes a novel framework for automatically detecting wind turbines in orthophotos, transferring this information to a database, and linking detected turbines to an existing registry to minimize location inaccuracy. This inaccuracy has a significant impact on planning and identifying new potential locations for wind turbines, as existing turbines must be considered in these processes. The existing public data frequently exhibit discrepancies from the actual location, and existing work also exhibits relatively large discrepancies from the actual location, even though the exact location of a wind turbine is so important for these processes. Moreover, existing work has not produced a new or improved database that could be used in the long term for processes in the wind energy sector. The development of the AutoWindLoc framework creates a fully automated data basis from which locations and possible further information can be retrieved. The recognition process utilizes a two-stage approach, incorporating a You Only Look Once model with negative sampling and a binary classification Convolutional Neural Network, which attains an average deviation of 0.85m from the actual location.

C. Jisha, Stefan Nolte, A. Alberucci

Geometric phase is a unifying and central concept in physics, including optics. As a matter of fact, optics played a pivotal role from the inception of this new paradigm, as some of the first experimental demonstrations have been carried out in optics. A specific type of geometric phase was first introduced by Pancharatnam while investigating interference effects between different polarizations. This specific type of geometric phase, nowadays called the Pancharatnam–Berry phase, is related to the variation of light polarization, encompassing exotic properties when compared with the dynamic phase associated with the optical path. The most widespread manifestation of the Pancharatnam–Berry phase occurs in the presence of a twisted anisotropic material, yielding a point‐wise phase modulation proportional to the local rotation angle of the material. Here the basic mechanism behind the Pancharatnam–Berry phase is discussed. The various applications of this relatively original concept in photonics are then reviewed, presenting both the most important results and manufactured devices reported in literature. The interplay between geometric phase and diffraction occurring in bulk structures is discussed in detail. In the latter case it is shown how geometric phase can be harnessed to generate a new kind of optical waveguide without the necessity of any index gradient.

Xueyue Zhang, Qi-Tao Cao, Zhuo Wang et al.

Lauren Dallachiesa, Ivan Biaggio

We introduce electrically-poled small molecule assemblies that can serve as the active electro-optic material in nano-scale guided-wave circuits such as those of the silicon photonics platform. These monolithic organic materials can be vacuum-deposited to homogeneously fill nanometer-size integrated-optics structures, and electrically poled at higher temperatures to impart an orientational non-centrosymmetric order that remains stable at room temperature. An initial demonstration using the DDMEBT molecule and corona poling delivered a material with the required high optical quality, an effective glass transition temperature of the order of $\sim 80$ $^\circ$C, and an electro-optic coefficient of $20$~pm/V.

Kunhao Ji, David J. Richardson, Stefan Wabnitz et al.

We introduce a novel all-optical platform in multimode and multicore fibres. By using a low-power probe beam and a high-power counter-propagating control beam, we achieve advanced and dynamic control over light propagation within the fibres. This setup enables all-optical reconfiguration of the probe, which is achieved by solely tuning the control beam power. Key operations such as fully tuneable power splitting and mode conversion, core-to-core switching and combination, along with remote probe characterization, are demonstrated at the sub-nanosecond time scale. Our experimental results are supported by a theoretical model that extends to fibres with an arbitrary number of modes and cores. The implementation of these operations in a single platform underlines its versatility, a critical feature of next-generation photonic systems. These results represent a significant shift from existing methods that rely on electro-optical or thermo-optical modulation for tunability. They pave the way towards a fast and energy-efficient alternative through all-optical modulation, a keystone for the advancement of future reconfigurable optical networks and optical computing. Scaling these techniques to highly nonlinear materials could underpin ultrafast all-optically programmable integrated photonics.

M. Recla, M. Schmitt

Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented approach is the first of its kind to implicitly handle the transition from the SAR-specific slant range geometry to a ground-based mapping geometry within the model architecture. Comparative experiments demonstrate the superiority of the dual-aspect fusion over single-image methods in terms of reconstruction quality and geolocation accuracy. Notably, the model exhibits robust performance across diverse acquisition modes and geometries, showcasing its generalizability and suitability for height mapping applications. The study’s findings underscore the potential of deep learning-driven SAR techniques in generating high-quality urban surface models efficiently and economically.

G. M. Perihanoglu, H. Karaman

Signal strength maps are of great importance for cellular system providers in network planning and operation. Accurate prediction of signal strength is important for solving problems such as link quality. In this study, Received Signal Strength (RSS) prediction model is proposed for the 900 MHz band in the Van Yüzüncü Yıl University campus environment by using machine learning regression methods such as K- Nearest Neıghbours (KNN) and Support Vector Regression (SVR) together with Geographic Information Systems. For the training of this model, signal strength values taken from the RF Spectrum Analyser at different locations and distances were used. In addition, spatial data sets such as the digital elevation model, location of base stations and measurement stations, building heights and location, and land use/cover were used in the model. The effect of these data sets on RSS power is included in the model. The model aims to predict RSS accurately, visualize the estimated signal strength, and analyze the signal field strength coverage. Different kernels from the SVR model such as Polynomial, , and Sigmoid were tested. To increase the success of the model, appropriate parameter values were selected and configured according to SVR and KNN methods. For 900 MHz, the performances of SVR and KNN models were compared and the results of the models were verified using root mean squares (RMSE). Among the measured data, the lowest prediction is found in KNN Manhattan. According to the results of the simulation was observed that the SVR model created with spatial data performs better for Signal Strength. Finally, the lowest RMSE value (1.71 dB) was obtained from the Sigmoid kernel in the best signal strength estimation SVR model. The SVR model is recommended for Campus Area signal strength estimation.

Safara Bibi, Guillermo Huerta-Cuellar, José Luís Echenausía-Monroy et al.

We present an innovative method harnessing multistability within a diode-pumped erbium-doped fiber laser to construct logic gates. Our approach involves manipulating the intensity of external noise to regulate the probability of transitioning among four concurrent attractors. In this manner, we facilitate the realization of OR, AND, NOR, and NAND logic operations, aligning with the coexisting period-1, period-3, period-4, and period-5 orbits. Employing detrended fluctuation analysis, we establish equilibrium in the probability distributions of these states. The obtained results denote a substantial advancement in the field of optical logic gate development, representing a pivotal stride toward the seamless integration of an all-optical logic gate within laser oscillator-based systems.

I. Roumpos, Lorenzo De Marinis, M. Kirtas et al.

In this paper, we introduce optics-informed Neural Networks and demonstrate experimentally how they can improve performance of End-to-End deep learning models for IM/DD optical transmission links. Optics-informed or optics-inspired NNs are defined as the type of DL models that rely on linear and/or nonlinear building blocks whose mathematical description stems directly from the respective response of photonic devices, drawing their mathematical framework from neuromorphic photonic hardware developments and properly adapting their DL training algorithms. We investigate the application of an optics-inspired activation function that can be obtained by a semiconductor-based nonlinear optical module and is a variant of the logistic sigmoid, referred to as the Photonic Sigmoid, in End-to-End Deep Learning configurations for fiber communication links. Compared to state-of-the-art ReLU-based configurations used in End-to-End DL fiber link demonstrations, optics-informed models based on the Photonic Sigmoid show improved noise- and chromatic dispersion compensation properties in fiber-optic IM/DD links. An extensive simulation and experimental analysis revealed significant performance benefits for the Photonic Sigmoid NNs that can reach below BER HD FEC limit for fiber lengths up to 42 km, at an effective bit transmission rate of 48 Gb/s.

Nitesh Chauhan, Jiawei Wang, D. Bose et al.

Atomic, molecular and optical (AMO) visible light systems are the heart of precision applications including quantum, atomic clocks and precision metrology. As these systems scale in terms of number of lasers, wavelengths, and optical components, their reliability, space occupied, and power consumption will push the limits of using traditional laboratory-scale lasers and optics. Visible light photonic integration is critical to advancing AMO based sciences and applications, yet key performance aspects remain to be addressed, most notably waveguide losses and laser phase noise and stability. Additionally, a visible light integrated solution needs to be wafer-scale CMOS compatible and capable of supporting a wide array of photonic components. While the regime of ultra-low loss has been achieved at telecommunication wavelengths, progress at visible wavelengths has been limited. Here, we report the lowest waveguide losses and highest resonator Qs to date in the visible range, to the best of our knowledge. We report waveguide losses at wavelengths associated with strontium transitions in the 461 nm to 802 nm wavelength range, of 0.01 dB/cm to 0.09 dB/cm and associated intrinsic resonator Q of 60 Million to 9.5 Million, a decrease in loss by factors of 6x to 2x and increase in Q by factors of 10x to 1.5x over this visible wavelength range. Additionally, we measure an absorption limited loss and Q of 0.17 dB/m and 340 million at 674 nm. This level of performance is achieved in a wafer-scale foundry compatible Si3N4 platform with a 20 nm thick core and TEOS-PECVD deposited upper cladding oxide, and enables waveguides for different wavelengths to be fabricated on the same wafer with mask-only changes per wavelength. These results represent a significant step forward in waveguide platforms that operate in the visible, opening up a wide range of integrated applications that utilize atoms, ions and molecules including sensing, navigation, metrology and clocks.

Yuchen Wang, K. Jöns, Zhipei Sun

Assisted by the rapid development of photonic integrated circuits, scalable and versatile chip-based quantum light sources with nonlinear optics are increasingly tangible for real-world applications. In this review, we introduce the basic concepts behind parametric photon pair sources and discuss the current state-of-the-art photon pair generation in detail but also highlight future perspectives in hybrid integration, novel waveguide structures, and on-chip multiplexing. The advances in near-deterministic integrated photon pair sources are deemed to pave the way for the realization of large-scale quantum photonic integrated circuits for applications, including quantum telecommunication, quantum sensing, quantum metrology, and photonic quantum computing.

M. Segev, M. Bandres

Abstract Topological photonics is currently one of the most active research areas in optics and also one of the spearheads of research in topological physics at large. We are now more than a decade after it started. Topological photonics has already proved itself as an excellent platform for experimenting with concepts imported from condensed matter physics. But more importantly, topological photonics has also triggered new fundamental ideas of its own and has offered exciting applications that could become real technologies in the near future.

Yanxin Ji, A. Cortese, Conrad L. Smart et al.

A general transfer method is presented for the heterogeneous integration of different photonic and electronic materials systems and devices into a single substrate. Called BLAST, for Bond, Lift, Align, and Slide Transfer, the process works at wafer scale and offers precision alignment, high yield, varying topographies, and suitability for subsequent lithographic processing. BLAST's capabilities is demonstrated by integrating both GaAs and GaN µLEDs with silicon photovoltaics to fabricate optical wireless integrated circuits that up‐convert photons from the red to the blue. The study also shows that BLAST can be applied to a variety of other devices and substrates, including CMOS electronics, vertical cavity surface emitting lasers (VCSELs), and 2D materials. BLAST further enables the modularization of optoelectronic microsystems, where optical devices fabricated on one material substrate can be lithographically integrated with electronic devices on a different substrate in a scalable process.

P. Es’haghi, Abolfazl Safaei Bezgabadi