循环cGAN驱动的1 × 2 MMI合器的反向设计,具有任意的功率分割比率和高效率
Optics express
|August 13, 2025
概括
本研究介绍了一个循环一致的条件生成对抗网络 (循环cGAN),用于设计多模干扰 (MMI) 合器. 人工智能模型有效地产生任意的功率分割比率,而光子学的多余损失较低.
科学领域:
- 光子学是指光子学的使用方法.
- 集成光学 集成光学 集成光学
- 人工智能的人工智能
背景情况:
- 多模干扰 (MMI) 合器是光子集成电路中的关键光学功率分割器.
- 实现随意的功率分割比率与低的多余损失 (ELs) 是具有挑战性的,需要手动调整和高计算成本.
研究的目的:
- 引入一个新的循环一致的条件生成对抗网络 (循环cGAN) 以实现高效的MMI合器设计.
- 克服在实现任意功率分割比率和MMI合器中低过量损失方面的局限性.
主要方法:
- 将条件生成对抗网络 (cGAN) 集成为反向设计模型.
- 利用完全连接的神经网络作为循环一致性验证的前预测模型.
- 在 1550 nm 波长的 1x2 MMI 合器设计中的应用.
主要成果:
- 循环cGAN有效地生成MMI合器设计,功率分割比为1:99至99:1.
- 在所有生成的设计中,多余损失 (ELs) 保持在0.7dB以下.
- 与传统方法相比,计算需求大幅减少.
结论:
- 拟议的循环cGAN为推进光子学和光子集成提供了一个有前途的解决方案.
- 该方法确保了物理可行的设计,并降低了计算成本.
- 允许MMI合器的高效设计,具有精确的功率分割能力.
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