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Conferences > FiO > 2022 > JTu4B > JTu4B.35

Design of silicon Mach-Zehnder modulators employing deep neural networks

Rômulo de Paula, Yesica R.R. Bustamante, Tiago Sutili, Rafael C. Figueiredo, Marcelo Luis F. Abbade, and Ivan Aldaya

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Abstract

We present an efficient optimization method for the design of silicon-on-insulator integrated Mach-Zehnder modulators based on a deep neural network that is capable of predicting the main figures of merit from the modulator design variables.

© 2022 The Author(s)

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