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Advances in photonics and nanotechnology have the potential to revolutionize humanity's ability to communicate and compute. To pursue these advances, it is mandatory to understand and properly model interactions of light with materials such as silicon and gold at the nanoscale, i.e., the span of a few tens of atoms laid side by side. These interactions are governed by the fundamental Maxwell's equations of classical electrodynamics, supplemented by quantum electrodynamics.This book presents the current state-of-the-art in formulating and implementing computational models of these interactions. Maxwell's equations are solved using the finite-difference time-domain (FDTD) technique, pioneered by the senior editor, whose prior Artech House books in this area are among the top ten most-cited in the history of engineering. You discover the most important advances in all areas of FDTD and PSTD computational modeling of electromagnetic wave interactions.This cutting-edge resource helps you understand the latest developments in computational modeling of nanoscale optical microscopy and microchip lithography. You also explore cutting-edge details in modeling nanoscale plasmonics, including nonlocal dielectric functions, molecular interactions, and multi-level semiconductor gain. Other critical topics include nanoscale biophotonics, especially for detecting early-stage cancers, and quantum vacuum, including the Casimir effect and blackbody radiation.

Many complex systems operate with loss. Mathematically, these systems can be described as non-Hermitian. A property of such a system is that there can exist certain conditions—exceptional points—where gain and loss can be perfectly balanced and exotic behavior is predicted to occur. Optical systems generally possess gain and loss and so are ideal systems for exploring exceptional point physics. Miri and Alù review the topic of exceptional points in photonics and explore some of the possible exotic behavior that might be expected from engineering such systems. Science , this issue p. eaar7709 Exceptional points are branch point singularities in the parameter space of a system at which two or more eigenvalues, and their corresponding eigenvectors, coalesce and become degenerate. Such peculiar degeneracies are distinct features of non-Hermitian systems, which do not obey conservation laws because they exchange energy with the surrounding environment. Non-Hermiticity has been of great interest in recent years, particularly in connection with the quantum mechanical notion of parity-time symmetry, after the realization that Hamiltonians satisfying this special symmetry can exhibit entirely real spectra. These concepts have become of particular interest in photonics because optical gain and loss can be integrated and controlled with high resolution in nanoscale structures, realizing an ideal playground for non-Hermitian physics, parity-time symmetry, and exceptional points. As we control dissipation and amplification in a nanophotonic system, the emergence of exceptional point singularities dramatically alters their overall response, leading to a range of exotic optical functionalities associated with abrupt phase transitions in the eigenvalue spectrum. These concepts enable ultrasensitive measurements, superior manipulation of the modal content of multimode lasers, and adiabatic control of topological energy transfer for mode and polarization conversion. Non-Hermitian degeneracies have also been exploited in exotic laser systems, new nonlinear optics schemes, and exotic scattering features in open systems. Here we review the opportunities offered by exceptional point physics in photonics, discuss recent developments in theoretical and experimental research based on photonic exceptional points, and examine future opportunities in this area from basic science to applied technology.

Significance Artificial systems that replicate functional attributes of the skins of cephalopods could offer capabilities in visual appearance modulation with potential utility in consumer, industrial, and military applications. Here we demonstrate a complete set of materials, components, fabrication approaches, integration schemes, bioinspired designs, and coordinated operational modes for adaptive optoelectronic camouflage sheets. These devices are capable of producing black-and-white patterns that spontaneously match those of the surroundings, without user input or external measurement. Systematic experimental, computational, and analytical studies of the optical, electrical, thermal, and mechanical properties reveal the fundamental aspects of operation and also provide quantitative design guidelines that are applicable to future embodiments.

Optical frequency conversion has applications ranging from tunable light sources to telecommunications-band interfaces for quantum information science. Here, we demonstrate efficient, low-noise frequency conversion on a nanophotonic chip through four-wave-mixing Bragg scattering in compact (footprint <0.5 × 10 –4  cm 2 ) Si 3 N 4 microring resonators. We investigate three frequency conversion configurations: spectral translation over a few nanometres within the 980 nm band; upconversion from 1,550 nm to 980 nm; and downconversion from 980 nm to 1,550 nm. With conversion efficiencies ranging from 25% for the first process to >60% for the last two processes, a signal conversion bandwidth of >1 GHz, a required continuous-wave pump power of <60 mW and background noise levels between a few femtowatts and a few picowatts, these devices are suitable for quantum frequency conversion of single-photon states from InAs/GaAs quantum dots. Simulations based on coupled mode equations and the Lugiato–Lefever equation are used to model device performance, and show quantitative agreement with measurements. Ultralow-noise frequency conversion within the 980-nm band and between the 980-nm and 1,550-nm bands occurs through Bragg scattering in Si 3 N 4 microring resonators. The maximum conversion efficiencies are 25% and 60%, respectively.

With the help of a quantum simulator and Bayesian inference it is possible to determine the unknown Hamiltonian of a quantum system. An experiment demonstrates this using a photonic quantum simulator and a solid-state system. The efficient characterization of quantum systems 1 , 2 , 3 , the verification of the operations of quantum devices 4 , 5 , 6 and the validation of underpinning physical models 7 , 8 , 9 , are central challenges for quantum technologies 10 , 11 , 12 and fundamental physics 13 , 14 . The computational cost of such studies could be improved by machine learning enhanced by quantum simulators 15 , 16 . Here we interface two different quantum systems through a classical channel—a silicon-photonics quantum simulator and an electron spin in a diamond nitrogen–vacancy centre—and use the former to learn the Hamiltonian of the latter via Bayesian inference. We learn the salient Hamiltonian parameter with an uncertainty of approximately 10 −5 . Furthermore, an observed saturation in the learning algorithm suggests deficiencies in the underlying Hamiltonian model, which we exploit to further improve the model. We implement an interactive version of the protocol and experimentally show its ability to characterize the operation of the quantum photonic device.

Doping a semiconductor with foreign atoms enables the control of its electrical and optical properties. We transplant the concept of doping to macroscopic photonics, demonstrating that two-dimensional dielectric particles immersed in a two-dimensional epsilon-near-zero medium act as dopants that modify the medium’s effective permeability while keeping its effective permittivity near zero, independently of their positions within the host. The response of a large body can be tuned with a single impurity, including cases such as engineering perfect magnetic conductor and epsilon-and-mu-near-zero media with nonmagnetic constituents. This effect is experimentally demonstrated at microwave frequencies via the observation of geometry-independent tunneling. This methodology might provide a new pathway for engineering electromagnetic metamaterials and reconfigurable optical systems.

We propose a surface plasmon resonance (SPR) sensor based on photonic crystal fiber (PCF) with selectively filled analyte channels. Silver is used as the plasmonic material to accurately detect the analytes and is coated with a thin graphene layer to prevent oxidation. The liquid-filled cores are placed near to the metallic channel for easy excitation of free electrons to produce surface plasmon waves (SPWs). Surface plasmons along the metal surface are excited with a leaky Gaussian-like core guided mode. Numerical investigations of the fiber’s properties and sensing performance are performed using the finite element method (FEM). The proposed sensor shows maximum amplitude sensitivity of 418 Refractive Index Units (RIU−1) with resolution as high as 2.4 × 10−5 RIU. Using the wavelength interrogation method, a maximum refractive index (RI) sensitivity of 3000 nm/RIU in the sensing range of 1.46–1.49 is achieved. The proposed sensor is suitable for detecting various high RI chemicals, biochemical and organic chemical analytes. Additionally, the effects of fiber structural parameters on the properties of plasmonic excitation are investigated and optimized for sensing performance as well as reducing the sensor’s footprint.

The potential of inertial measurement units (IMUs) for ergonomics applications appears promising. However, previous IMUs validation studies have been incomplete regarding aspects of joints analysed, complexity of movements and duration of trials. The objective was to determine the technological error and biomechanical model differences between IMUs and an optoelectronic system and evaluate the effect of task complexity and duration. Whole-body kinematics from 12 participants was recorded simultaneously with a full-body Xsens system where an Optotrak cluster was fixed on every IMU. Short functional movements and long manual material handling tasks were performed and joint angles were compared between the two systems. The differences attributed to the biomechanical model showed significantly greater ( P  ≤ .001) RMSE than the technological error. RMSE was systematically higher ( P  ≤ .001) for the long complex task with a mean on all joints of 2.8° compared to 1.2° during short functional movements. Definition of local coordinate systems based on anatomical landmarks or single posture was the most influent difference between the two systems. Additionally, IMUs accuracy was affected by the complexity and duration of the tasks. Nevertheless, technological error remained under 5° RMSE during handling tasks, which shows potential to track workers during their daily labour.

Penetration depth of ultraviolet, visible light and infrared radiation in biological tissue has not previously been adequately measured. Risk assessment of typical intense pulsed light and laser intensities, spectral characteristics and the subsequent chemical, physiological and psychological effects of such outputs on vital organs as consequence of inappropriate output use are examined. This technical note focuses on wavelength, illumination geometry and skin tone and their effect on the energy density (fluence) distribution within tissue. Monte Carlo modelling is one of the most widely used stochastic methods for the modelling of light transport in turbid biological media such as human skin. Using custom Monte Carlo simulation software of a multi-layered skin model, fluence distributions are produced for various non-ionising radiation combinations. Fluence distributions were analysed using Matlab mathematical software. Penetration depth increases with increasing wavelength with a maximum penetration depth of 5378 μm calculated. The calculations show that a 10-mm beam width produces a fluence level at target depths of 1–3 mm equal to 73–88% (depending on depth) of the fluence level at the same depths produced by an infinitely wide beam of equal incident fluence. Meaning little additional penetration is achieved with larger spot sizes. Fluence distribution within tissue and thus the treatment efficacy depends upon the illumination geometry and wavelength. To optimise therapeutic techniques, light-tissue interactions must be thoroughly understood and can be greatly supported by the use of mathematical modelling techniques.

Optical computers and photonic integrated circuits in high capacity optical networks are hot topics, attracting the attention of expert researchers and commercial technology companies. Optical packet switching and routing technologies promise to provide a more efficient source of power, and footprint scaling with increased router capacity; integrating more optical processing elements into the same chip to increase on-chip processing capability and system intelligence has become a priority. This book is an in-depth look at modelling techniques and the simulation of a wide range of liquid crystal based modern photonic devices with enhanced high levels of flexible integration and enhanced power processing. It covers the physics of liquid crystal materials; techniques required for modelling liquid crystal based devices; the state-of-the art liquid crystal photonic based applications for telecommunications such as couplers, polarization rotators, polarization splitters and multiplexer-demultiplexers; liquid core photonic crystal fiber (LC-PCF) sensors including biomedical and temperature sensors; and liquid crystal photonic crystal based encryption systems for security applications. Key features * Offers a unique source of in-depth learning on the fundamental principles of computational liquid crystal photonics. * Explains complex concepts such as photonic crystals, liquid crystals, waveguides and modes, and frequency- and time-domain techniques used in the design of liquid crystal photonic crystal photonic devices in terms that are easy to understand. * Demonstrates the useful properties of liquid crystals in a diverse and ever-growing list of technological applications. * Requires only a foundational knowledge of mathematics and physics.