Over the past 15 years since their first demonstration, subwavelength grating metamaterials in silicon photonic devices have become widely used and attracted rapidly growing research interest while also breaking into commercial applications. We will discuss recent advances in this research field, with a focus on novel components and circuits for beam steering applications, on-chip filtering and quantum optics. On-chip optical waveguides comprised of Mie resonant particle chains have only recently been demonstrated and promise to be the foundation of a new and exciting branch of integrated metamaterials research. We will review the early work in this area.
Silicon photonics platform has undergone substantial development to tackle future challenges of various applications, including datacom, sensing, and optical communications. Numerous efficient devices and circuits have been proposed, and products are already available in the market. However, the intrinsic properties of silicon-based materials do not fully overcome the limitations in terms of speed, power consumption and scalability.
As a result, new strategies have emerged, mainly focusing on the integration of new materials and the exploitation of nonlinear optical properties. In this context, the paper will present the current status and prospects of Si and SiGe photonics and its potential applications.
Photonic integration offers great potentialities for the realization of compact, light-weight, and low-cost systems for free-space applications, noteworthy in the field of 3D imaging and optical communications. However, several shortcomings still limit the widespread applicability of integrated solution, e.g., low efficiencies, narrow operational bandwidth, and polarization sensitivity. The use of metamaterial and metasurfaces, combined with innovative design approaches, represents a powerful tool to overcome these limitations. In this talk, we will present our recent advances in the realization of highly-performing devices for free-space applications and optical beam control, with a particular focus on integrated grating antennas based on metamaterials and metasurfaces.
Silicon Nitride photonic integrated circuits are highly sought after for quantum applications. This platform offers ultra-low propagation losses, reduced birefringence, and a wide transparency window. This study presents the design and experimental demonstration of a compact silicon nitride polarization beam splitter (PBS) for the 950 nm wavelength range. The PBS employs cascaded tapered asymmetric directional couplers to achieve efficient polarization control. With insertion losses below 1 dB, polarization extinction ratios exceeding 19 dB (TE) and 10 dB (TM), and operation from 920 nm to 970 nm, it offers promising integration into photonic systems requiring precise polarization manipulation.
Innovative photonic devices and systems aim at achieving simultaneously a large scale of integration and high performance, leading to complex designs based on metamaterials and non-trivial geometries characterized by a large number of geometrical and material parameters. At the same time, modern cutting-edge designs usually involve multiple deterministic and stochastic figures of merit that account for both performance metrics and fabrication requirements, thus complicating the selection of the final design candidates. In this invited talk, we will discuss the potentiality of combining multi-objective analysis and optimization tools with machine learning techniques for the design of highly performing photonic devices and systems.
We report the demonstration of an integrated silicon-rich silicon nitride wavelength converter based on the Bragg scattering intermodal four-wave mixing process. This broadband wavelength converter incorporates on-chip mode conversion, multiplexing and de-multiplexing functionalities. The system allows for broadband signal conversion with a 3dB bandwidth exceeding 70 nm.
Subwavelength metamaterials allow to synthesize tailored optical properties which enabled the demonstration of photonic devices with unprecedented performance and scale of integration. Yet, the development of metamaterial-based devices often involves a large number of interrelated parameters and figures of merit whose manual design can be impractical or lead to suboptimal solutions. In this invited talk, we will discuss the potentiality offered by multi-objective optimization and machine learning for the design of high-performance photonic devices based on metamaterials. We will present both integrated devices for on-chip photonic systems as well as recent advances in the development of devices for free-space applications and optical beam control.
Optical phased arrays in silicon photonics are an emerging technology for free-space communications and light detection and ranging (LIDAR). While traditional LIDARs with discrete components and mechanical beam steering are difficult to integrate and scale, silicon-based arrays have taken a massive leap forward in developing beam steering systems with compact footprint and high performance on a single chip. Here, we report our results in the development of chip-scale circular phased arrays. Arrays formed in a grid of concentric rings are shown to suppress the sidelobes, expand the steering range and obtain narrower beamwidths, with large spacing between optical elements.
Design of novel integrated photonic components often benefits from periodic geometries (either fully periodic or apodized) along the direction of light propagation, offering a wide range of capabilities including mode matching and optical rerouting. Here, we show how existing iterative methods that were originally developed for resonant nanophotonic systems in the frequency domain can be reliably used for calculation of optical Bloch modes in periodic systems in the complex wavevector domain. This method can be used for arbitrary shaped geometries and even when open boundary conditions are applied, therefore heavily impacting the fast-paced design of integrated photonic devices.
The widespread use of metamaterials and non-trivial geometries has radically changed the way photonic integrated devices are developed, opening new design possibility and allowing for unprecedented performance. Yet, these devices are often described by a large number of interrelated parameters which cannot be handled manually, requiring innovative design approaches for their effective optimization. In this invited talk, we will discuss the potentiality offered by the combination of machine learning dimensionality reduction and multi-objective optimization for the design of high performance photonic integrated devices.
Design of modern integrated nanophotonic components requires increasingly sophisticated optical simulation and optimization tools. Modeling and computational challenges arise with the increase in the number of design parameters and the introduction of multiple and often competing performance criteria. In such high dimensional design parameter spaces, it becomes difficult to navigate, explore, and visualize the best candidate designs that satisfy all the requirements. We present our recently developed approach that leverages dimensionality reduction - an area of machine learning – to identify and efficiently investigate only the most relevant portion of the design space. Once this reduced space is found, mapping and optimization can often be achieved several orders of magnitude faster than in the original design space. We showcase our approach on several design scenarios focusing on components such as optical grating couplers and power splitters. We employ principal component analysis for linear dimensionality reduction, achieving impressive performance despite its simplicity. We also demonstrate the use of a non-linear technique, i.e. neural-network based autoencoders, which can improve the effectiveness of dimensionality reduction even further. All components have nontrivial regions of interest in their design space that are identified and explored through the evaluation of various performance metrics. Visualizations of these regions offer a global picture of device behavior. Different component geometries can then be chosen depending on specific performance requirements or fabrication constraints. The proposed framework can be easily integrated into various design toolkits.
We are demonstrating the use of Low temperature PECVD silicon nitride based materials used for applications ranging from non-linear functionalities in the C band, wavelength division multiplexing in the O band and post fabrication light based refractive index tuning for in-situ device trimming. These materials are demonstrated for waveguide ranging from 300 nm up to 1 micron in thickness with refractive indices varying between 1.9 and 2.55.
Modern design of photonic devices is quickly and steadily departing from classical geometries to focus more and more on non-trivial structures and metamaterials. These devices are governed by a multitude of parameters and the optimal design requires to simultaneously consider different figure of merits. In this invited talk we will present our recent work on the application of machine learning tools to the multi-objective optimization of multi-parameter photonic devices. In particular, we will demonstrate the potentiality of dimensionality reduction for the analysis of the complex design space of subwavelength metamaterials devices.
We present fully apodized and perfectly vertical surface grating couplers in 300 nm silicon waveguides. We achieve ultrahigh coupling efficiency of -0.35 dB at 1550 nm for an optical fiber with 10.4 µm mode-fiber-diameter. To this end, we followed a two stage process in which, we first optimized a pool of periodic grating couplers using machine learning techniques, and then apodized them over 100 parameters using the gradient based adjoint- method. Using a simple fabrication tolerance analysis, we also show that segment variations mostly causes a wavelength shift for the maximum coupling efficiency of the apodized grating couplers, similar to those typically observed for periodic grating couplers.
Exoplanetary biosignatures, molecular compounds which indicate a likelihood of extraterrestrial life, can be detected by highly sensitive spectroscopy of starlight which passes through the atmospheres of exoplanets towards the Earth. Such sensitive measurements can only be accomplished with the next generation of telescopes, leading to a corresponding increase in cost and complexity spectrometers. Integrated astrophotonic instruments are well-suited to address these challenges through their low-cost fabrication and compact geometries. We propose and characterize an integrated photonic gas sensor which detects the correlation between the near-infrared quasi-periodic vibronic absorption line spectrum of a gas and a silicon waveguide ring resonator transmittance comb. This technique enables lock-in amplification detection for real-time detection of faint biosignatures for reduced observation timescales and rapid exoplanetary atmosphere surveys using highly compact instrumentation.
Enabled by technological improvements, photonic devices and circuits are becoming increasingly more complex. Non-trivial geometries are designed to reduce device footprint, improve performance, and introduce novel functionalities. However, the number of design variables required to properly represent these geometries quickly grows, limiting the effectiveness of classical design approaches. Moreover, parameters are often strongly interdependent, restricting the use of sequential optimizations or independent parameter sweeps. Although several optimization techniques can be effective for multi-parameter design, they commonly allow to optimize for a single or a handful designs and the optimization process needs to be repeated if new performance criteria are introduced. In contrast to classical design approaches, the in uences of the design parameters remain hidden as well as the general behavior of the design space. In this paper we present an extension of our recent work on the application of machine learning pattern recognition to the design of multi-parameter photonic devices. In particular, we propose using a combination of local optimization based on the adjoint method and the use of dimensionality reduction. Adjoint optimization is used multiple times to generate a small set of different designs with high performance. Dimensionality reduction is applied to analyze the relationship between these degenerate designs and identify a lower-dimensional design sub-space that includes all alternative good designs. This sub-space can be mapped for any performance criteria thus enabling informed decisions based on the relative priorities of all relevant performance specifications. As a proof of concept, we demonstrate a ten-parameter design of an integrated photonic power splitter using silicon-on-insulator technology. We identify a region of possible high performance design solutions and select two design candidates either maximizing the splitter efficiency or minimizing back-reflection.
Astronomical instrumentation is traditionally costly, large, and alignment-sensitive owing to the use of bulk optics. The use of integrated photonic devices in astronomical instrumentation can mitigate such drawbacks in certain applications where high light throughput and spectral bandwidth are less crucial. In this work, we present an ultra-compact carbon dioxide detection scheme using a single silicon waveguide ring resonator. The comb-like absorption line spectrum of CO2 around 1580 nm wavelength can closely match the comb spectrum of an appropriately designed ring resonator. By actively correlating such a ring spectrum with the CO2 absorption lines, a specific detection signal can be generated. We design the free spectral range of a ring resonator to match the absorption line spacing of carbon dioxide lines in the range from 1575 to 1585 nm. Using thermo-optic modulation, the ring resonator drop or through port transmission spectrum can be shifted back and forth across the incoming CO2 light spectrum, resulting in a modulated signal with an amplitude proportional to the CO2 absorption line strength. Furthermore, high frequency modulation and lock-in detection can result in a significant improvement in the signal to noise ratio. We demonstrate that such a device can provide real-time carbon dioxide detection for applications in ground- and satellite-based astronomy, as well as remote atmospheric sensing, in a compact package. In future work, such a sensor can be adapted to a range of gases and used to determine radial velocities and compositional maps of astronomical objects.
The performance and functionality of integrated photonic devices can be enhanced by using complex structures controlled by a large number of design variables. However, the optimization of such high-dimensional structures is challenging, often limiting their realization. Global optimization algorithms and artificial neural networks are increasingly used to tackle these problems. Although these are exciting new developments, the outcome is a single optimized design meeting particular performance objectives selected upfront. The influences of the various design parameters remain hidden. Here we report on our strategy of using machine learning pattern recognition techniques to create a methodology for building the global performance map of a high-dimensional design space. As an example and demonstration, we study the design of a vertical grating coupler consisting of silicon and subwavelength metamaterial segments. We show how the relationship between designs with comparable primary performance can be clearly revealed by identifying the minimum number of characterizing parameters that defines the subspace of good designs, significantly scaling down the complexity of the problem. Moreover, the subspace can be identified using only a small number of good design solutions. We reveal design areas with comparable fiber coupling efficiency but with significant differences in other performance criteria, such as back-reflections, tolerance to fabrication uncertainty and minimum feature size. This novel approach provides the designer with a global perspective of the design space, enabling informed decisions based on the relative priorities of all relevant performance specifications and figures-of-merits for a particular application. Insights from the mapping exercise also inspired new design structures with enhanced characteristics.
Machine-assisted design of integrated photonic devices (e.g. through optimization and inverse design methods) is opening the possibility of exploring very large design spaces, novel functionalities and non-intuitive geometries. These methods are generally used to optimize performance figures-of-merit. On the other hand, the effect of manufacturing variability remains a fundamental challenge since small fabrication errors can have a significant impact on light propagation, especially in high-index-contrast platforms. Brute-force analysis of these variabilities during the main optimization process can become prohibitive, since a large number of simulations would be required. To this purpose, efficient stochastic techniques integrated in the design cycle allow to quickly assess the performance robustness and the expected fabrication yield of each tentative device generated by the optimization. In this invited talk we present an overview of the recent advances in the implementation of stochastic techniques in photonics, focusing in particular on stochastic spectral methods that have been regarded as a promising alternative to the classical Monte Carlo method. Polynomial chaos expansion techniques generate so called surrogate models by means of an orthogonal set of polynomials to efficiently represent the dependence of a function to statistical variabilities. They achieve a considerable reduction of the simulation time compared to Monte Carlo, at least for mid-scale problems, making feasible the incorporation of tolerance analysis and yield optimization within the photonic design flow.
Temperature sensitivity is an issue that severely affects many integrated silicon photonic devices. Proper circuit functionality is normally ensured by active thermal control at the expense of energy consumption. In some cases, athermal behavior can be achieved exploiting cladding materials with a negative thermo-optic coefficient to counterbalance the positive coefficients of silicon and silica. On the other hand, in echelle grating filters this method is not effective because in the slab free-propagation region the modal overlap with the cladding is small, especially for TEpolarized light. Moreover the need to add non-standard materials to the established silicon-on-insulator (SOI) fabrication process could make these solutions impractical. Here we present the design of a temperature-insensitive echelle grating demultiplexer with four channels operating in the TE polarization that does not use any materials with negative thermooptic coefficient and relies exclusively on standard processes for SOI photonics. The design exploits a temperaturesynchronized Mach-Zehnder interferometer as input to the echelle to compensate the shift of the imaged field with temperature. The device achieves a significant reduction in the temperature dependence of the overall transmission with a residual channel wavelength fluctuation smaller than 45 pm over a temperature range of 20 K, compared to a 1.6-nm shift for the same grating with a conventional waveguide input. The excess loss due to the use of the Mach-Zehnder input is no more than 0.7 dB for all four channels. Furthermore, the proposed design shows a very good tolerance to fabrication uncertainty, with minimum degradation of the performance for waveguide width variations of 10 nm.
Integrated nanophotonic component design processes are often constrained by computational resources. Advances in simulation and optimization tools have allowed more efficient exploration of larger design spaces. These developments reduce the time-consuming and intuition-limited effort of encoding physical insights into the design structure. However, we argue that efficient optimization is only part of the solution to tackle larger multi-parameter design spaces. Finding patterns in such a space can be more valuable than identifying the individual optima alone. This is particularly true when transitioning from simulation to real device fabrication, where considerations such as tolerance to fabrication imperfections, bandwidth, etc. take an important role but are ignored at the optimization stage. The elucidation of patterns in a complex design space enables efficient identification of designs addressing these additional considerations. As an example, in this presentation we demonstrate how limited data collected from the optimization process of a multisegment vertical grating coupler can be used to identify such patterns through the application of machine learning techniques. The identified patterns, some more interpretable than others, can be used in multiple ways: from speeding up the remaining optimization process itself to gaining insight into the properties of an interesting subset of designs. Together those insights offer a significantly clearer picture of the design space and form the basis for making much more informed decisions on the final designs to be fabricated.
Unavoidable statistical variations in fabrication processes have a strong effect on the functionality of fabricated photonic circuits and on fabrication yield. It is hence essential to measure and consider these uncertainties during the design in order to predict the statistical behavior of the realized circuits. Also, during the mass production of photonic integrated circuits, the experimental evaluation of circuits’ desired quantity of interest in the presence of fabrication error can be crucial. In this paper we proposed the use of generalized polynomial chaos method to estimate the statistical properties of a circuit from a reduced number of experimental data whilst achieving good accuracy comparable to those obtained by Monte Carlo.
The Building Block (BB) approach has recently emerged in photonic as a suitable strategy for the analysis and design of complex circuits. Each BB can be foundry related and contains a mathematical macro-model of its functionality. As well known, statistical variations in fabrication processes can have a strong effect on their functionality and ultimately affect the yield. In order to predict the statistical behavior of the circuit, proper analysis of the uncertainties effects is crucial. This paper presents a method to build a novel class of Stochastic Process Design Kits for the analysis of photonic circuits. The proposed design kits directly store the information on the stochastic behavior of each building block in the form of a generalized-polynomial-chaos-based augmented macro-model obtained by properly exploiting stochastic collocation and Galerkin methods. Using this approach, we demonstrate that the augmented macro-models of the BBs can be calculated once and stored in a BB (foundry dependent) library and then used for the analysis of any desired circuit. The main advantage of this approach, shown here for the first time in photonics, is that the stochastic moments of an arbitrary photonic circuit can be evaluated by a single simulation only, without the need for repeated simulations. The accuracy and the significant speed-up with respect to the classical Monte Carlo analysis are verified by means of classical photonic circuit example with multiple uncertain variables.
The interface between the core and the cladding of optical waveguides exhibits a number of physical phenomena that do
not occur in the bulk of the material. For this reason, the behavior of nanoscale devices is expected to be conditioned, or
even dominated, by the nature of their surfaces. Roughness-induced losses, backscattering and crosstalk between
adjacent waveguides, together with surface states absorption impact on the optical and electrical properties of the
waveguides must be considered in the design of any integrated optoelectronic device. The detrimental effects and the
possibility of their exploitation are carefully reviewed, presenting in particular the ContacLess Integrated Photonic Probe
to be used as transparent power monitor.
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