We report a monochrome multi-task diffractive network architecture that leverages illumination phase multiplexing to dynamically reconfigure its output function and accurately implement a large group of complex-valued linear transformations between an input and output aperture. Each member of the desired group of T unique transformations is encoded and addressed with a distinct 2D illumination phase profile, termed "phase key", which illuminates the input aperture, activating the corresponding transformation at the output field-of-view. A common diffractive optical network, optimized with T phase keys, demultiplexes these encoded inputs and accurately executes any of the T distinct linear transformations at its output. We demonstrate that a diffractive network composed of N = 2 x T x Ni x No optimized diffractive features can realize T distinct complex-valued linear transformations, accurately executed for any complex field at the input aperture, where Ni and No refer to the input/output pixels, respectively. In our proof-of-concept numerical analysis, T = 512 complex-valued transformations are implemented by the same monochrome diffractive network with negligible error using illumination phase diversity. Compared with wavelength-multiplexed diffractive systems, phase-multiplexing architecture significantly lowers the transformation errors, potentially enabling larger-scale optical transformations to be implemented through a monochrome processor. Phase-multiplexed multi-task diffractive networks would enhance the capabilities of optical computing and machine-vision systems.

翻译：本文报道了一种单色多任务衍射网络架构，该架构利用照明相位复用技术动态重构其输出功能，并在输入与输出孔径之间精确实现一大类复值线性变换。所需变换集合中每个T个独特变换的成员均通过一个独特的二维照明相位分布（称为“相位密钥”）进行编码与寻址，该相位密钥照射输入孔径，从而在输出视场中激活相应的变换。一个经过T个相位密钥优化的公共衍射光学网络对这些编码输入进行解复用，并在其输出端精确执行任意T个不同的线性变换。我们证明，由N = 2 × T × Ni × No个优化衍射特征组成的衍射网络可实现T个不同的复值线性变换，且能对输入孔径处的任意复值场精确执行变换，其中Ni和No分别代表输入/输出像素数。在概念验证数值分析中，同一单色衍射网络通过照明相位多样性实现了T = 512个复值变换，误差可忽略不计。与波长复用衍射系统相比，相位复用架构显著降低了变换误差，有望通过单色处理器实现更大规模的光学变换。相位复用多任务衍射网络将增强光学计算与机器视觉系统的能力。