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arXiv 1809.03555physics.app-phcond-mat.mes-hallphysics.optics
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集成纳米光子器件的深度神经网络逆设计

Deep Neural Network Inverse Design of Integrated Nanophotonic Devices

Mohammad H. Tahersima, Keisuke Kojima, Toshiaki Koike-Akino, Devesh Jha, Bingnan Wang, Chungwei Lin, Kieran Parsons

摘要:

预测人工结构材料的物理响应对于科学和工程应用特别感兴趣。在这里,我们使用深度学习来预测人工设计的纳米光子器件的光学响应。除了预测任何给定拓扑结构的传输响应的正向近似外,这种方法还允许我们逆近似设计目标光学响应。我们的深度神经网络(DNN)可以在几分之一秒内设计出紧凑的(2.6x2.6 μm2)基于绝缘体上硅(SOI)的1 X 2功率分配器,具有各种目标分束比。该模型经过训练,以最大限度地减少反射(小于20 dB),同时实现最大传输效率(高于90%)和目标分裂规格。这种方法为快速设计依赖于复杂纳米结构的集成光子器件铺平了道路。

英文摘要:

Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6x2.6 μm2) silicon-on-insulator (SOI)-based 1 X 2 power splitters with various target splitting ratios in a fraction of a second. This model is trained to minimize the reflection (smaller than 20 dB) while achieving maximum transmission efficiency (above 90%) and target splitting specifications. This approach paves the way for rapid design of integrated photonic components relying on complex nanostructures.

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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