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<p>This paper presents a new reinforcement learning (RL)-driven inverse design strategy that leverages the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for the efficient optimization of photonic structures, with a focus on metamaterial absorbers (MAs) and cross polarization converters (CPC) as demonstrative applications. Unlike conventional heuristic or surrogate-based optimization methods, the proposed RL approach autonomously learns the optimal geometric configuration through direct interaction with the simulation environment, without requiring gradient information or pre-built surrogate models. Initially, the TD3 model is used to optimize the geometric parameters of an existing MA based on an L-shaped resonator, significantly enhancing its absorption performance to be greater than 90% in the frequency range from 12.2&#xa0; GHz to 22.4&#xa0; GHz in only 23 iterations. Then, a novel CPC design is proposed, optimized using the same RL framework, and subsequently fabricated. The fabricated structure achieves high polarization conversion ratio (PCR) above 90% over a wide frequency range from 11.8&#xa0; GHz to 24.2&#xa0; GHz, covering the full Ku band and most of the K band. Furthermore, over most of the frequency range, the converter maintains strong performance under oblique incidence, with PCR levels above 80% up to an angle of 50<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(^\circ\)</EquationSource> </InlineEquation>. These results validate the effectiveness of the TD3-based RL framework in discovering high-performance and fabrication-ready designs, while also establishing a scalable and generalizable optimization paradigm for advanced photonic devices.</p>

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Optimization of broadband metamaterial absorber using twin delayed deep deterministic policy gradient reinforcement learning technique

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月18日

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  • Basant E. Mahmoud,
  • Tamer A. Ali,
  • Salah S. A. Obayya,
  • Mohamed Farhat O. Hameed

摘要

This paper presents a new reinforcement learning (RL)-driven inverse design strategy that leverages the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for the efficient optimization of photonic structures, with a focus on metamaterial absorbers (MAs) and cross polarization converters (CPC) as demonstrative applications. Unlike conventional heuristic or surrogate-based optimization methods, the proposed RL approach autonomously learns the optimal geometric configuration through direct interaction with the simulation environment, without requiring gradient information or pre-built surrogate models. Initially, the TD3 model is used to optimize the geometric parameters of an existing MA based on an L-shaped resonator, significantly enhancing its absorption performance to be greater than 90% in the frequency range from 12.2  GHz to 22.4  GHz in only 23 iterations. Then, a novel CPC design is proposed, optimized using the same RL framework, and subsequently fabricated. The fabricated structure achieves high polarization conversion ratio (PCR) above 90% over a wide frequency range from 11.8  GHz to 24.2  GHz, covering the full Ku band and most of the K band. Furthermore, over most of the frequency range, the converter maintains strong performance under oblique incidence, with PCR levels above 80% up to an angle of 50 \(^\circ\) . These results validate the effectiveness of the TD3-based RL framework in discovering high-performance and fabrication-ready designs, while also establishing a scalable and generalizable optimization paradigm for advanced photonic devices.