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Updated: Sep 11, 2025

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带有改进的U-net边缘检测的接转移后补偿策略,以减少25Gbps的合损失TOSA TOSA
Optics express
|August 13, 2025
概括
我们使用MPFG-net开发了一种深度学习方法,以弥补光电子设备的接后转移. 这种技术通过准确识别接点和优化对齐来提高光学功率,实现15%的功率增益.
科学领域:
- 光电子设备包装的包装
- 深度学习在工程中的应用.
- 光学合器的光学合器
背景情况:
- 接后转移是光电子设备包装的一个关键问题,导致显著的功率减弱.
- 精确识别接点特征对于有效的接后转移补偿至关重要.
- 现有的方法难以准确识别局部接点特征.
研究的目的:
- 提出一种新的补偿方法,用于光电子设备的接后转移.
- 通过深度学习提高接点特征识别的准确性.
- 通过精确对齐和激光能量调制来改善光学功率补偿.
主要方法:
- 实现基于U-net的深度学习架构,称为MPFG-net.
- 集成CG-MLP和MIF模块,以改善局部接点特征识别.
- 开发一种补偿策略,将MPFG网络基调与激光能量调制相结合.
主要成果:
- MPFG-net实现了78%的特殊接点边缘识别精度.
- 拟议的网络在图像识别性能方面表现优于主要主流网络.
- 补偿策略导致光功率补偿提高了15%.
结论:
- MPFG-net架构有效地解决了光电子设备中接后转换的挑战.
- 基于深度学习的图像识别显著提高了接点对齐的精度.
- 拟议的方法为光电子包装的光功率补偿提供了显著的改善.
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