基于重复相位编码的结构非线性深度逆设计纳米光子处理器

中文摘要 AI 辅助

无源纳米光子散射区可实现线性光学变换，仅级联与输入无关的区域无法产生功能深度，因为所得变换会坍缩为单一线性算子。本文在逆设计的无源变换之间引入重复相位编码，以生成无需层间光电探测的输入条件型多层光学映射。每次重新编码都会引入额外的相位相关光学路径，从而相对于编码变量产生结构非线性，而每个散射区在光场中仍保持无源和线性。在受控MNIST深度扫描下，分类准确率从1层的83.53%提升至7层的93.61%，而与输入无关的无源对照组准确率饱和在86.34%。该深度趋势在时分复用CIFAR-10补丁模型中同样存在。我们进一步实现了三个联合训练的16×16变换，每个变换独立作为逆设计纳米光子区实现，其相对复透射误差分别为7.63%、7.59%和8.80%。经相位校准后，重构的电磁堆栈准确率达91.79%，而其代理模型的准确率为91.95%。这些结果确立了重复输入编码是紧凑逆设计纳米光子处理器中功能深度的实现途径。

英文摘要

Passive nanophotonic scattering regions implement linear optical transformations, and cascading input-independent regions alone does not create functional depth because the resulting transformations collapse into a single linear operator. Here, we introduce repeated phase encoding between inverse-designed passive transformations to generate an input-conditioned multilayer optical map without interlayer photodetection. Each re-encoding introduces additional phase-dependent optical pathways, producing structural nonlinearity with respect to the encoded variables while every scattering region remains passive and linear in the optical field. Under a controlled MNIST depth sweep, classification accuracy increases from 83.53\% with one layer to 93.61\% with seven layers, whereas the input-independent passive control saturates at 86.34\%. The depth trend also persists in a time-multiplexed CIFAR-10 patch model. We further realize three jointly trained $16\times16$ transformations, each independently implemented as an inverse-designed nanophotonic region, with relative complex transmission errors of 7.63\%, 7.59\%, and 8.80\%. The reconstructed electromagnetic stack reaches 91.79\% accuracy after phase calibration, compared with 91.95\% for its surrogate model. These results establish repeated input encoding as a route to functional depth in compact inverse-designed nanophotonic processors.

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19 pages, 6 figures