Wavelength and composition dependence of the thermo-optic coefficient for InGaAsP-based integrated waveguides

A method to take into account the wavelength, composition, and temperature dependencies in the calculation of the refractive index and linear thermo-optic coefficient of In1−xGaxAsyP1−y alloys is presented. The method, based on the modified single oscillator model, shows a good agreement with experimental data for InP reported in literature at different wavelength and temperature ranges. Further, we exploit this approach with a Film-Mode Matching solver to calculate the linear thermo-optic coefficients of both phase and group effective indices of an InGaAsP-based waveguide. The same waveguide structure is also experimentally investigated through a reflectometric technique and results are found to be in accordance with the simulations performed exploiting the proposed method. In both cases, a dependence of the group index on temperature, almost twice that of the phase index, is observed. These results provide a deeper understanding on the influence of the temperature on the behaviour of optical waveguides and devices, making possible an accurate and realistic modelling of integrated circuits

Stochastic process design kits for photonic circuits based on polynomial chaos augmented macro-modelling

Fabrication tolerances can significantly degrade the performance of fabricated photonic circuits and process yield. It is essential to include these stochastic uncertainties in the design phase in order to predict the statistical behaviour of a device before the final fabrication. This paper presents a method to build a novel class of stochastic-based building blocks for the preparation of Process Design Kits for the analysis and design of photonic circuits. The proposed design kits directly store the information on the stochastic behaviour 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 these macro-models, only a single deterministic simulation is required to compute the stochastic moments of any arbitrary photonic circuit, without the need of running a large number of time-consuming circuit simulations thereby dramatically improving simulation efficiency. The effectiveness of the proposed approach is verified by means of classical photonic circuit examples with multiple uncertain variables

Machine-assisted design and stochastic analysis in integrated photonics

Integrated photonic devices are steadily making their way into many application fields including modern optical communication networks and advanced sensors. On the other hand, the design of photonic devices and circuits mostly remains a time-consuming process largely based on the designer experience. This limits the size and complexity of the parameter space that can be handled. Moreover, addressing 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 such as silicon-on-insulator. The analysis of this variability with conventional approaches (e.g. Monte Carlo) can become prohibitive due to the large number of required simulations. Recent advances in machine-assisted design methods are opening the possibility to vastly expand the number of design parameters, exploring novel functionalities and non-intuitive geometries. In this invited talk we discuss the use of machine learning methods for the design of integrated photonic devices. We show the existence of a large number of possible designs that are all equivalent with respect to a given primary design objective but with distinct properties in other performance criteria. We use pattern recognition to reveal their relationship and to reduce the dimensionality of the large design space by properly defining new design variables. Likewise, we show how efficient stochastic techniques allow a quick assessment of the performance robustness and the expected fabrication yield for each tentative device. We focus in particular on stochastic spectral methods that have been regarded as a promising alternative to the classical Monte Carlo method, achieving a considerable reduction of the simulation time. Together, the reduction in the design space dimensionality and efficient stochastic techniques allow for the integration of the fabrication tolerance considerations into the design process

Global design optimization in photonics: from high performance to fabrication robustness

Modern photonic devices are characterized by a large number of parameters and the need for an “holistic” optimization of their behavior taking into account multiple figures of merit, noteworthy tolerance to fabrication uncertainty. We present here a set of tools based on dimensionality reduction capable of handling such multi-parameter, multi-objectives design problems

Dimensionality reduction and optimization for the inverse design of photonic integrated 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 device

Subwavelength metamaterial devices with optimization and machine learning

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

Wideband Integrated Optical Delay Line Based on a Continuously Tunable Mach-Zehnder Interferometer

An integrated optical delay line is presented and experimentally demonstrated with a true-time delay continuously tuned up to 125 ps. The proposed device is based on a Mach-Zehnder interferometer with tuneable couplers, can be ideally operated with a single control signal, and achieves a bandwidth-delay product consistently larger than ring-based delay lines. The device is successfully used in a transmission system to control the delay of a 10 Gbit/s data stream

Stochastic simulation and sensitivity analysis of photonic circuit through Morris and Sobol method

Two different sensitivity analysis methods are applied to the coupled ring resonator filter to assess how the fabrication processes variation of some geometrical parameters can influence the performance of the photonics devices

Sensitivity Analysis and Uncertainty Mitigation of Photonic Integrated Circuits

Unavoidable statistical variations in fabrication processes can have a strong effect on the functionality of fabricated photonic circuits and on fabrication yield. It is hence essential to consider these uncertainties during design in order to predict and control the statistical behavior of the circuits. In this paper, we exploit elementary effect test and variance-based sensitivity analysis to investigate the behavior of a photonic circuit under fabrication uncertainties, with the aim to identify the most critical parameters affecting circuit performances. As an example, we perform the sensitivity analysis on the 3-dB bandwidth of two different filter designs considering random deviations of the waveguides width and couplers' gap. The information obtained from the analysis is then used to isolate the most critical parameters of the circuits and to estimate and reduce the cost of postfabrication correction of the process variability