Reconfigurable photonic integrated mode (de)multiplexer for SDM fiber transmission

A photonic integrated circuit for mode multiplexing and demultiplexing in a few-mode fiber is presented and demonstrated. Two 10 Gbit/s channels at the same wavelength and polarization are simultaneously transmitted over modes LP01 and LP11a of a few-mode fiber exploiting the integrated mode MUX and DEMUX. The proposed Indium-Phosphide-based circuits have a good coupling efficiency with fiber modes with mode-dependant loss smaller than 1 dB. Measured mode excitation cross-talk is as low as -20 dB and a channel cross-talk after propagation and demultiplexing of -15 dB is achieved. An operational bandwidth of the full transmission system of at least 10 nm is demonstrated. Both mode MUX and DEMUX are fully reconfigurable and allow a dynamic switch of channel routing in the transmission system

Stochastic simulation and robust design optimization of integrated photonic filters

Manufacturing variations are becoming an unavoidable issue in modern fabrication processes; therefore, it is crucial to be able to include stochastic uncertainties in the design phase. In this paper, integrated photonic coupled ring resonator filters are considered as an example of significant interest. The sparsity structure in photonic circuits is exploited to construct a sparse combined generalized polynomial chaos model, which is then used to analyze related statistics and perform robust design optimization. Simulation results show that the optimized circuits are more robust to fabrication process variations and achieve a reduction of 11%–35% in the mean square errors of the 3 dB bandwidth compared to unoptimized nominal designs.MIT Skoltech InitiativeProgetto Roberto Rocca (Seed Funds)National Science Foundation (U.S.) (AIM Photonics Center. Contract 1227020-EEC)Semiconductor Research Corporatio

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

Optical wavefront phase-tilt measurement using Si-photonic waveguide grating couplers

Silicon photonic wavefront phase-tilt sensors for wavefront monitoring using surface coupling grating arrays are demonstrated. The first design employs the intrinsic angle dependence of the grating coupling efficiency to determine local wavefront tilt, with a measured sensitivity of 7 dB/degree. A second design connects four gratings in an interferometric waveguide circuit to determine incident wavefront phase variation across the sensor area. In this device, one fringe spacing corresponds to approximately 2 degree wavefront tilt change. These sensor elements can sample a wavefront incident on the chip surface without the use of bulk optic elements, fiber arrays, or imaging arrays. Both sensor elements are less than 60 um across, and can be combined into larger arrays to monitor wavefront tilt and distortion across an image or pupil plane in adaptive optics systems for free space optical communications, astronomy and beam pointing applications

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