Deep Learning-Based Inverse Design Enabling a Highly Efficient Multimode Interference Coupler

This work aims to simplify the design of multimode interference (MMI) couplers and enhance coupling efficiency. A neural network is proposed to automatically optimize the output power of the MMI coupler and derive its parameters.

Authors

Menglong Luo (ORCID: https://orcid.org/0000-0001-8982-9672)
Sang‐Shin Lee (ORCID: https://orcid.org/0000-0001-5686-4893)

Institutions

Kwangwoon University (KR)

Publication Details

Published: 2024-08-04
DOI: https://doi.org/10.1109/cleo-pr60912.2024.10676453
Primary Topic: Antenna Design and Optimization
Type: article
Field-Weighted Citation Impact: 0.00

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article

Deep Learning-Based Inverse Design Enabling a Highly Efficient Multimode Interference Coupler

Menglong Luo, Sang‐Shin Lee

Aug 4, 2024

Antenna Design and Optimization

article

Deep Learning-Based Inverse Design Enabling a Highly Efficient Multimode Interference Coupler

Menglong Luo, Sang‐Shin Lee

August 4, 2024article en

Abstract

Kwangwoon University (KR)

Openalex Percentile: Top 76%

Antenna Design and Optimization

DOI Source OpenAlex

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Deep Learning-Based Inverse Design Enabling a Highly Efficient Multimode Interference Coupler — Menglong Luo, Sang‐Shin Lee · (2024) | TGRS Research Map | TGRS