| DOI | Resolve DOI: https://doi.org/10.1117/12.2508602 |
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| Author | Search for: Melati, Daniele1; Search for: Grinberg, Yuri2; Search for: Waqas, Abi; Search for: Manfredi, Paolo; Search for: Kamandar Dezfouli, Mohsen1; Search for: Cheben, Pavel1; Search for: Schmid, Jens H.1; Search for: Janz, Siegfried1; Search for: Melloni, Andrea; Search for: Xu, Dan-Xia1 |
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| Editor | Search for: Lee, El-Hang; Search for: He, Sailing |
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| Affiliation | - National Research Council of Canada. Advanced Electronics and Photonics
- National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | Smart Photonic and Optoelectronic Integrated Circuits XXI, February 2-7, 2019, San Francisco, United States |
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| Subject | Monte Carlo methods; stochastic processes; photonic devices; waveguides; machine learning; optical design; optimization (mathematics) |
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| Abstract | 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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| Publication date | 2019-03-04 |
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| Publisher | SPIE |
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| In | |
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| Series | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 241652b8-6b0e-41b7-9d64-8968fd70f1a7 |
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| Record created | 2021-07-16 |
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| Record modified | 2021-07-19 |
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