arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.03747physics.opticseess.SPphysics.comp-ph
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多层纳米光宽带功分器的生成式深度学习模型

Generative Deep Learning Model for a Multi-level Nano-Optic Broadband Power Splitter

Yingheng Tang, Keisuke Kojima, Toshiaki Koike-Akino, Ye Wang, Pengxiang Wu, Mohammad Tahersima, Devesh K. Jha, Kieran Parsons, Minghao Qi

摘要:

我们提出了一种新的条件变分自动编码器(CVAE)模型,增强了对抗审查和主动学习,用于生成具有任意分光比的550 nm宽带(1250 nm至1800 nm)功率分配器。器件尺寸为2.25 x 2.25 μ m2,具有20 x 20蚀刻孔组合。这是首次将CVAE模型和对抗性删失应用于光子学问题。我们确认,优化后的器件在1250 nm至1800 nm的所有带宽范围内的整体性能接近90%。据我们所知,这是最小的宽带功率分配器与任意比例。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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