Combining deep learning and nano-optics as a new enabling technology for nano-scale characterization and information processing

尽管经典的计算技术能够解决复杂的数学问题，但在其他人类通常可以轻松解决的问题上却相当糟糕，例如图像或语音识别。在过去的十年中，人工神经网络（ANN）领域取得了巨大的进展，ANN是受人脑工作原理启发的计算模型。人工神经网络可以被训练来对这些问题进行分类，并最终非常有效地解决它们。DFG研究项目的目标是将深度人工神经网络应用于纳米光学领域。第一步，人工神经网络将用于预测光子纳米结构和超表面的光学特性。在我为准备这一提案所做的初步研究中，我已经证明了神经网络用于复杂光子纳米结构的光学散射的超快速预测的能力。通过用实验数据集训练ANN，我将获得用于预测光学效应的完全唯象模型。在第二个工作包中，深度学习技术将通过解决“逆”问题--预测提供所需光学响应的纳米结构几何形状--应用于纳米光学器件的设计。除了单个纳米光学元件的概念，我将探索深度学习方法在复杂的多模态系统中的应用，例如基于散斑的压缩传感或全光可重构光子路由以及光学信息编码。

Despite their ability to solve complicated mathematical problems, classical computational techniques are pretty bad at other tasks, which humans usually solve without any difficulty.Such problems include for instance image or speech recognition.During the last decade, great progress has been made in the field of artificial neural networks (ANNs) - computational models inspired by how the human brain works. ANNs can be trained to categorize such problems and to eventually solve them very efficiently.The goal of this DFG research project is to apply deep artificial neural networks to the field of nano-optics.In a first step, ANNs will be used for the prediction of optical properties of photonic nano-structures and meta-surfaces.In preliminary studies that I have done in preparation of this proposal, I have demonstrated the capability of neural networks for the ultra-rapid prediction of the optical scattering of complex photonic nanostructures.By training ANNs with experimental datasets, I will obtain fully phenomenological models for the prediction of optical effects.In a second work package, deep learning techniques will be applied to the design of nano-optical devices by solving "inverse" problems -- the prediction of nanostructure geometries which offer a desired optical response.Next to the conception of individual nano-optical components, I will explore the use of deep learning methods in complex, multi-modal systems such as speckle-based compressive sensing or all-optical reconfigurable photonic routing and for optical information encoding.