| Authors: | Y. Xing, D. Spina, A. Li, T. Dhaene, W. Bogaerts | | Title: | Variability analysis of device-level photonics using stochastic collocation | | Format: | International Conference Proceedings | | Publication date: | 5/2016 | | Journal/Conference/Book: | SPIE Photonics Europe 2016
| | Location: | Brussels, Belgium | | DOI: | 10.1117/12.2224037 | | Citations: | Look up on Google Scholar
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Abstract
Abstract Integrated photonics, and especially silicon photonics, has been rapidly expanded its catalog of building blocks and functionalities. Now, it is maturing fast towards circuit-level integration to serve more complex applications in industry. However, performance variability due to the fabrication process and operational conditions can limit the yield of large-scale circuits. It is essential to assess this impact at the design level with an efficient variability analysis: how variations in geometrical, electrical and optical parameters propagate into components performance. In particular when implementing wavelength-selective filters, many primary functional parameters are affected by fabrication-induced variability. The key functional parameters that we assess in this paper are the waveguide propagation constant (the effective index, essential to define the exact length of a delay line) and the coupling coefficients in coupling structure (necessary to set the power distribution over different delay lines). The Monte Carlo (MC) method is the standard method for variability analysis, thanks to its accuracy and easy implementation. However, due to its slow convergence, it requires a large set of samples (simulations or measurements), making it computationally or experimentally expensive. More efficient methods to assess such variability can be used, such as generalized polynomial chaos (gPC) expansion or stochastic collocation. Related Research Topics
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