Design of task-specific optical systems using broadband diffractive neural networks.,Light: Science & Applications

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Design of task-specific optical systems using broadband diffractive neural networks.
Light: Science & Applications ( IF 20.6 ) Pub Date : 2019-12-02 , DOI: 10.1038/s41377-019-0223-1
Yi Luo _{1,

2,

3} , Deniz Mengu _{1,

2,

3} , Nezih T Yardimci _{1,

3} , Yair Rivenson _{1,

2,

3} , Muhammed Veli _{1,

2,

3} , Mona Jarrahi _{1,

3} , Aydogan Ozcan _{1,

2,

3,

4}

Affiliation

Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.

中文翻译：

使用宽带衍射神经网络设计特定任务的光学系统。

深度学习在许多领域都具有变革性，从而激发了各种光学计算架构的出现。衍射光学网络是最近引入的光学计算框架，该框架将波光学与深度学习方法融合在一起以设计光学神经网络。据报道，通过该框架设计并通过3D打印制造的基于衍射的全光学对象识别系统可识别手写数字和时尚产品，展示了全光学推理和对数据子类的概括。这些先前的衍射方法采用单色相干光作为照明源。这里，我们报告了一种宽带衍射光学神经网络设计，该设计可同时处理由时间上不相干的宽带光源产生的连续波长，以全光学方式执行通过深度学习获得的特定任务。我们通过设计，制造和测试七个不同的多层衍射光学系统，对宽带THz脉冲产生的光波阵面进行变换，以实现（1）一系列可调谐的，单波长的，从而实验性地验证了这种宽带衍射神经网络架构的成功。通带和双通带频谱滤波器以及（2）空间控制的波长多路分解。将各种材料系统的本机或工程分散体与基于深度学习的设计策略合并，

更新日期：2019-12-02

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