论文标题
多层纳米光线宽带功率分离器的生成深度学习模型
Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter
论文作者
论文摘要
我们提出了一种新型的条件变异自动编码器（CVAE）模型，并通过对抗性检查和主动学习增强，以生成550 nm的宽带宽（1250 nm至1800 nm）功率拆分器，并具有任意拆卸比。设备足迹为2.25 x2.25μm2，具有20 x 20蚀刻孔组合。这是第一次将CVAE模型和对抗性审查用于光子问题问题的演示。我们确认，优化的设备的总体性能在所有带宽中的总体性能接近90％，从1250 nm到1800 nm。据我们所知，这是具有任意比例的最小宽带功率分离器。
We propose a novel Conditional Variational Autoencoder (CVAE) model, enhanced with adversarial censoring and active learning, for the generation of 550 nm broad bandwidth (1250 nm to 1800 nm) power splitters with arbitrary splitting ratio. The device footprint is 2.25 x 2.25 μ m2 with a 20 x 20 etched hole combination. It is the first demonstration to apply the CVAE model and the adversarial censoring for the photonics problems. We confirm that the optimized device has an overall performance close to 90% across all bandwidths from 1250 nm to 1800 nm. To the best of our knowledge, this is the smallest broadband power splitter with arbitrary ratio.
