Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11034
Title: Machine learning enabled the design of compact and efficient wavelength demultiplexing photonic devices
Authors: Turduev, M.
Bor, E.
Alparslan, O.
Hanay, Y.S.
Kurt, H.
Arakawa, S.
Murata, M.
Keywords: inverse design
machine learning
wavelength demultiplexing
Issue Date: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: In this paper, we introduce the design approach of integrated photonic devices by employing reinforcement learning known as attractor selection (AttSel). Here, we combined 3D FDTD with AttSel algorithm, which is based on artificial neural networks, to achieve ultra-compact and highly efficient wavelength demultiplexers with low crosstalk such as. The presented devices consist of SOI materials, which are compatible with complementary MOS technology. Consequently, the reinforcement learning is successfully applied to design smaller and superior integrated photonic devices. © 2023 IEEE.
Description: 2023 IEEE Photonics Conference, IPC 2023 -- 12 November 2023 through 16 November 2023 -- 195842
URI: https://doi.org/10.1109/IPC57732.2023.10360715
https://hdl.handle.net/20.500.11851/11034
ISBN: 9798350347227
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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