Performance robustness analysis in machine-assisted design of photonic devices

Daniele Melati; Yuri Grinberg; Abi Waqas; Paolo Manfredi; Mohsen Kamandar Dezfouli; Pavel Cheben; Jens H. Schmid; Siegfried Janz; Andrea Melloni; Dan-Xia Xu

doi:10.1117/12.2508602

4 March 2019 Performance robustness analysis in machine-assisted design of photonic devices

Daniele Melati, Yuri Grinberg, Abi Waqas, Paolo Manfredi, Mohsen Kamandar Dezfouli, Pavel Cheben, Jens H. Schmid, Siegfried Janz, Andrea Melloni, Dan-Xia Xu

Author Affiliations +

Proceedings Volume 10922, Smart Photonic and Optoelectronic Integrated Circuits XXI; 1092203 (2019) https://doi.org/10.1117/12.2508602
Event: SPIE OPTO, 2019, San Francisco, California, United States

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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Daniele Melati, Yuri Grinberg, Abi Waqas, Paolo Manfredi, Mohsen Kamandar Dezfouli, Pavel Cheben, Jens H. Schmid, Siegfried Janz, Andrea Melloni, and Dan-Xia Xu "Performance robustness analysis in machine-assisted design of photonic devices", Proc. SPIE 10922, Smart Photonic and Optoelectronic Integrated Circuits XXI, 1092203 (4 March 2019); https://doi.org/10.1117/12.2508602

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