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 |
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 WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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