Genetic algorithm and polynomial chaos modelling for performance optimization of photonic circuits under manufacturing variability

Melati, Daniele; Waqas, Abi; Xu, Dan‐Xia; Melloni, Andrea

doi:10.1364/ofc.2018.m3i.4

Cited by 7 publications

(7 citation statements)

References 4 publications

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“…One of the areas where a lot of work is ongoing is variability analysis and yield prediction for photonic integrated circuits [77], [78], [80]- [83]. We discuss this in more detail in the next section.…”

Section: Future Photonic Circuit Design Flowmentioning

confidence: 99%

Photonic Integrated Circuit Design in a Foundry+Fabless Ecosystem

Khan

Xing

et al. 2019

IEEE J. Select. Topics Quantum Electron.

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A foundry-based photonic ecosystem is expected to become necessary with increasing demand and adoption of photonics for commercial products. To make foundry-enabled photonics a real success, the photonic circuit design flow should adopt known concepts from analog and mixed signal electronics. Based on the similarities and differences between the existing photonic and the standardized electronics design flow, we project the needs and evolution of the photonic design flow, such as schematic driven design, accurate behavioral models, and yield prediction in the presence of fabrication variability.

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Section: Future Photonic Circuit Design Flowmentioning

confidence: 99%

Photonic Integrated Circuit Design in a Foundry+Fabless Ecosystem

Khan

Xing

et al. 2019

IEEE J. Select. Topics Quantum Electron.

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“…mean, variance, probability density function…) impractical. This problem can be particularly emphasized when the computation of the moments is required as part of an optimization routine because thousands of simulations would be required at each step of the optimization that could require itself several thousands of iterations [14].…”

Section: Surrogate Models With Polynomial Chaos Expansionmentioning

confidence: 99%

“…Accurate and efficient variability representation has been demonstrated for several photonic devices, also in the case of correlated random variables [11,12]. Models based on generalized polynomial chaos expansion have been recently exploited for circuit design optimization under fabrication uncertainty [13,14]. These techniques can provide a viable way to introduce uncertainty information at into machine-assisted design flows.…”

Section: Introductionmentioning

confidence: 99%

Performance robustness analysis in machine-assisted design of photonic devices

Melati

Grinberg

Waqas

et al. 2019

Smart Photonic and Optoelectronic Integrated Circuits XXI

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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.

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“…On the other hand, fully evaluating the statistical behavior of a device requires a large number of computations, with Monte Carlo simulation as the standard 'brute-force' method. Stochastic techniques such as polynomial chaos expansion have emerged as efficient alternatives to assess the performance robustness and expected fabrication yield [19,21,22]. Even with these advances, carrying out robustness assessment during the device optimization process requires prohibitive computation resources as well as being wasteful, since most evaluations would be carried out on structures that do not meet the primary objective.…”

Section: Global Mapping Of the Design Spacementioning

confidence: 99%

Navigating through complex photonic design space using machine learning methods

Grinberg

Melati

et al. 2019

Integrated Optics: Design, Devices, Systems, and Applications V

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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.

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Genetic algorithm and polynomial chaos modelling for performance optimization of photonic circuits under manufacturing variability

Abstract: We propose an efficient technique based on polynomial chaos expansion and genetic algorithms to enable constrained optimization of photonic integrated circuits subject to fabrication tolerances. Simulations on a realistic SOI design confirm its effectiveness.

Cited by 7 publications

References 4 publications

Photonic Integrated Circuit Design in a Foundry+Fabless Ecosystem

Photonic Integrated Circuit Design in a Foundry+Fabless Ecosystem

Performance robustness analysis in machine-assisted design of photonic devices

Navigating through complex photonic design space using machine learning methods

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