InP CL-MZM Design Optimization by Deep Learning and Genetic Algorithm

We present design optimization for InP-based capacitive loading traveling wave Mach-Zehnder modulator based on training an artificial neural network deep learning model. An ultrahigh-speed design with 108 GHz bandwidth is optimized by exploring the multi-parameter design space using our model and genetic algorithm search.

Authors

Ruoyun Yao (ORCID: https://orcid.org/0000-0002-4321-5584)
Chen Ji
Weiwei Pan (ORCID: https://orcid.org/0000-0001-8323-861X)

Institutions

Zhejiang University (CN)

Publication Details

Published: 2024-11-10
DOI: https://doi.org/10.1109/ipc60965.2024.10799784
Primary Topic: Photonic and Optical Devices
Type: article
Field-Weighted Citation Impact: 0.00

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article

InP CL-MZM Design Optimization by Deep Learning and Genetic Algorithm

Ruoyun Yao, Chen Ji, Weiwei Pan

Nov 10, 2024

Photonic and Optical Devices

article

InP CL-MZM Design Optimization by Deep Learning and Genetic Algorithm

Ruoyun Yao, Chen Ji, Weiwei Pan

November 10, 2024article en

Abstract

Zhejiang University (CN)

Openalex Percentile: Top 78%

Photonic and Optical Devices

DOI Source OpenAlex

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InP CL-MZM Design Optimization by Deep Learning and Genetic Algorithm — Ruoyun Yao, Chen Ji, et al. · (2024) | TGRS Research Map | TGRS