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通过深度学习和进化算法设计马赫-泽恩德调制器

Romulo Aparecido de Paula^1,2,3, Ivan Aldaya⁴, Tiago Sutili⁵

¹Center for Advanced and Sustainable Technologies, State University of Sao Paulo (UNESP), São João da Boa Vista, SP, 13876-750, Brazil. romulo.aparecido.22@ucl.ac.uk.

Scientific reports

|September 5, 2023

概括

本研究介绍了使用人工神经网络和启发式优化进行马赫-泽恩德调制器 (MZMs) 的新设计方法. 这种方法加速了对光通信系统的高性能MZM的优化.

科学领域:

光子学和光学工程的工程.
材料科学与工程材料科学与工程
计算科学计算科学

背景情况:

Mach-Zehnder调制器 (MZM) 是光通信中的关键组件,但优化它们在高速应用中的性能是具有挑战性的.
传统的MZM优化方法由于复杂的设备物理和众多设计参数,耗时且资源密集.

研究的目的:

为高性能Mach-Zehnder调制器开发一个显著更快,更有效的设计方法.
克服传统优化技术在实现最佳MZM配置方面的局限性.

主要方法:

实现了深度神经网络,以取代昂贵的3D电磁模拟MZM.
将神经网络模型与启发式优化算法 (差异进化) 集成在一起,以估计优点的数字.
应用了一种方法来优化CMOS兼容MZM的电光带宽,插入损失和半波电压.

主要成果:

实现了优化的MZM配置,增强了电光带宽和降低了驱动电压.
演示了特定的配置,产生[公式:查看文本]带宽与[公式:查看文本]驱动电压,或[公式:查看文本]带宽与[公式:查看文本]驱动电压.
允许在各种约束条件下更快地探索MZM的性能极限.

结论:

拟议的人工神经网络和启发式优化方法极大降低了MZM设计复杂性和模拟时间.
这种方法有助于发现新的,高性能的MZM设计,这对于推进光通信技术至关重要.
该方法允许在各种约束条件下进行高效的优化,推动MZMs的性能极限.

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