Veli Muhammed, Mengu Deniz, Yardimci Nezih T, Luo Yi, Li Jingxi, Rivenson Yair, Jarrahi Mona, Ozcan Aydogan
Department of Electrical and Computer Engineering, University of California Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Department of Bioengineering, University of California Los Angeles (UCLA), Los Angeles, CA, 90095, USA.
Nat Commun. 2021 Jan 4;12(1):37. doi: 10.1038/s41467-020-20268-z.
Recent advances in deep learning have been providing non-intuitive solutions to various inverse problems in optics. At the intersection of machine learning and optics, diffractive networks merge wave-optics with deep learning to design task-specific elements to all-optically perform various tasks such as object classification and machine vision. Here, we present a diffractive network, which is used to shape an arbitrary broadband pulse into a desired optical waveform, forming a compact and passive pulse engineering system. We demonstrate the synthesis of various different pulses by designing diffractive layers that collectively engineer the temporal waveform of an input terahertz pulse. Our results demonstrate direct pulse shaping in terahertz spectrum, where the amplitude and phase of the input wavelengths are independently controlled through a passive diffractive device, without the need for an external pump. Furthermore, a physical transfer learning approach is presented to illustrate pulse-width tunability by replacing part of an existing network with newly trained diffractive layers, demonstrating its modularity. This learning-based diffractive pulse engineering framework can find broad applications in e.g., communications, ultra-fast imaging and spectroscopy.
深度学习的最新进展一直在为光学中的各种逆问题提供非直观的解决方案。在机器学习与光学的交叉领域，衍射网络将波动光学与深度学习相结合，以设计特定任务的元件，从而全光地执行各种任务，如目标分类和机器视觉。在此，我们展示了一种衍射网络，它用于将任意宽带脉冲整形为所需的光波形，形成一个紧凑的无源脉冲工程系统。我们通过设计衍射层来共同调控输入太赫兹脉冲的时间波形，从而展示了各种不同脉冲的合成。我们的结果展示了太赫兹频谱中的直接脉冲整形，其中输入波长的幅度和相位通过一个无源衍射装置独立控制，无需外部泵浦。此外，还提出了一种物理迁移学习方法，通过用新训练的衍射层替换现有网络的一部分来说明脉冲宽度的可调性，展示了其模块化特性。这种基于学习的衍射脉冲工程框架可在例如通信、超快成像和光谱学等领域找到广泛应用。
