Mengu Deniz, Zhao Yifan, Tabassum Anika, Jarrahi Mona, Ozcan Aydogan
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
Nanophotonics. 2022 Sep 5;12(5):905-923. doi: 10.1515/nanoph-2022-0358. eCollection 2023 Mar.
Permutation matrices form an important computational building block frequently used in various fields including, e.g., communications, information security, and data processing. Optical implementation of permutation operators with relatively large number of input-output interconnections based on power-efficient, fast, and compact platforms is highly desirable. Here, we present diffractive optical networks engineered through deep learning to all-optically perform permutation operations that can scale to hundreds of thousands of interconnections between an input and an output field-of-view using passive transmissive layers that are individually structured at the wavelength scale. Our findings indicate that the capacity of the diffractive optical network in approximating a given permutation operation increases proportional to the number of diffractive layers and trainable transmission elements in the system. Such deeper diffractive network designs can pose practical challenges in terms of physical alignment and output diffraction efficiency of the system. We addressed these challenges by designing misalignment tolerant diffractive designs that can all-optically perform arbitrarily selected permutation operations, and experimentally demonstrated, for the first time, a diffractive permutation network that operates at THz part of the spectrum. Diffractive permutation networks might find various applications in, e.g., security, image encryption, and data processing, along with telecommunications; especially with the carrier frequencies in wireless communications approaching THz-bands, the presented diffractive permutation networks can potentially serve as channel routing and interconnection panels in wireless networks.
置换矩阵构成了一个重要的计算构建块，常用于包括通信、信息安全和数据处理等在内的各个领域。基于高效节能、快速且紧凑的平台，对具有相对大量输入输出互连的置换算子进行光学实现是非常可取的。在此，我们展示了通过深度学习设计的衍射光学网络，以全光方式执行置换操作，该操作可以扩展到使用在波长尺度上单独构建的无源透射层，在输入和输出视场之间实现数十万的互连。我们的研究结果表明，衍射光学网络在逼近给定置换操作方面的能力与系统中衍射层和可训练传输元件的数量成正比增加。这种更深层次的衍射网络设计在系统的物理对准和输出衍射效率方面可能会带来实际挑战。我们通过设计能够全光执行任意选择的置换操作的抗失准衍射设计来应对这些挑战，并首次通过实验证明了一种在太赫兹光谱部分运行的衍射置换网络。衍射置换网络可能会在安全、图像加密、数据处理以及电信等领域找到各种应用；特别是随着无线通信中的载波频率接近太赫兹频段，所展示的衍射置换网络有可能在无线网络中用作信道路由和互连面板。
