مؤتمر
Performance robustness analysis in machine-assisted design of photonic devices
| العنوان: | Performance robustness analysis in machine-assisted design of photonic devices |
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| المؤلفون: | Melati D., Grinberg Y., Waqas A., Manfredi P., Kamandar Dezfouli M., Cheben P., Schmid J. H., Janz S., Melloni A., Xu D. -X. |
| المساهمون: | Lee, El-Hang, Melati, D., Grinberg, Y., Waqas, A., Manfredi, P., Kamandar Dezfouli, M., Cheben, P., Schmid, J. H., Janz, S., Melloni, A., Xu, D. -X. |
| بيانات النشر: | SPIE 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA |
| سنة النشر: | 2019 |
| المجموعة: | RE.PUBLIC@POLIMI - Research Publications at Politecnico di Milano |
| مصطلحات موضوعية: | Machine learning, Pattern recognition, Photonic device, Principal component analysi, Probability theory, Silicon photonic, Stochastic processe, Uncertainty analysis |
| الوصف: | 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. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| العلاقة: | info:eu-repo/semantics/altIdentifier/isbn/9781510624863; info:eu-repo/semantics/altIdentifier/isbn/9781510624870; info:eu-repo/semantics/altIdentifier/wos/WOS:000468072600001; ispartofbook:Proceedings of SPIE - The International Society for Optical Engineering; Smart Photonic and Optoelectronic Integrated Circuits XXI 2019; volume:10922; firstpage:1; lastpage:2; numberofpages:2; serie:PROCEEDINGS OF SPIE, THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING; alleditors:Lee, El-Hang; http://hdl.handle.net/11311/1121628Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85065434095; http://spie.org/x1848.xmlTest |
| DOI: | 10.1117/12.2508602 |
| الإتاحة: | https://doi.org/10.1117/12.2508602Test http://hdl.handle.net/11311/1121628Test http://spie.org/x1848.xmlTest |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.AC8A3FB3 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1117/12.2508602 |
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