Presentation + Paper
23 February 2018 Uncertainty quantification and stochastic modelling of photonic device from experimental data through polynomial chaos expansion
Author Affiliations +
Abstract
Unavoidable statistical variations in fabrication processes have a strong effect on the functionality of fabricated photonic circuits and on fabrication yield. It is hence essential to measure and consider these uncertainties during the design in order to predict the statistical behavior of the realized circuits. Also, during the mass production of photonic integrated circuits, the experimental evaluation of circuits’ desired quantity of interest in the presence of fabrication error can be crucial. In this paper we proposed the use of generalized polynomial chaos method to estimate the statistical properties of a circuit from a reduced number of experimental data whilst achieving good accuracy comparable to those obtained by Monte Carlo.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abi Waqas, Daniele Melati, Zarlish Mushtaq, and Andrea Melloni "Uncertainty quantification and stochastic modelling of photonic device from experimental data through polynomial chaos expansion", Proc. SPIE 10535, Integrated Optics: Devices, Materials, and Technologies XXII, 105351A (23 February 2018); https://doi.org/10.1117/12.2290540
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Stochastic processes

Monte Carlo methods

Resonators

Chaos

Statistical analysis

Data modeling

Mathematical modeling

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