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A two-step optimization algorithm for enhanced sidelobe suppression ratio and robust beamforming in optical phased arrays

  • Jian Lin
  • , Jiachang Liang
  • , Xiaoping Liu
  • , Mingda Huang
  • , Feihu Wang
  • , Yuanfeng Wang
  • , Xiaopeng Hu
  • , Xiaoshan Wu
  • , Hongling Cai*
  • , Qi Qin*
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

As a compact integrated alternative to traditional microwave phased arrays, optical phased arrays (OPAs) have demonstrated great potential across a wide range of applications. Beamforming performance, particularly the sidelobe suppression ratio (SLSR), is crucial for signal quality and interference immunity. To enhance SLSR, we propose a hybrid two-step optimization algorithm that combines adaptive moment estimation (Adam) and particle swarm optimization (PSO). Using a composite variable splitter (VS), we achieve average SLSRs over a ±70° scanning range >20.14 dB for Bayliss difference patterns and >22.23 dB for Dolph-Chebyshev patterns. For asymmetric multi-beam patterns, the achieved average SLSRs are 20.14 dB, 19.10 dB, and 18.11 dB for the four-, six-, and eight-beam patterns, respectively. Robustness to VS splitting-ratio (SR) imbalance is further demonstrated using a traditional VS with SR constrained to ±13.80 dB, where the average SLSRs remain >19.07 dB and >20.87 dB for the Bayliss difference and Dolph-Chebyshev patterns and 18.42 dB, 17.12 dB, and 16.83 dB for the four-, six-, and eight-beam patterns, respectively. These results show that the proposed algorithm improves OPA beamforming quality while relaxing the VS precision required to support a wide SR tuning range, facilitating chip-level integration of large-scale, high-performance OPAs. © 2026 Elsevier B.V.
Original languageEnglish
Article number133379
Number of pages10
JournalOptics Communications
Volume616
Online published15 May 2026
DOIs
Publication statusPublished - Oct 2026

Funding

This work was supported by the National Natural Science Foundation of China, China (12474014), the National Nature Science Foundations of Jiangsu province, China (BK20241843), the Guangdong Provincial Quantum Science Strategic Initiative, China (GDZX2306001, GDZX2303001), the Shenzhen Fundamental research project, China (Grant No. JCYJ20241202123903005), the Nantong Natural Science Foundations, China (JCZ2023021, JCZ2024016), the Top-Notch Young Talents Program of China, China, the Dengfeng Project B of Nanjing University, China, the startup funding of City University of Hong Kong, Hong Kong SAR, China, and the Guangdong Basic and Applied Basic Research Foundation, China (Nos. 2024B1515020117 and 2023A1515012793).

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