Ultra-dense moving cascaded metasurface holography by using a physics-driven neural network
Hongqiang Zhou, Xin Li, He Wang, Shifei Zhang, Zhaoxian Su, Qiang Jiang, Naqeeb Ullah, Xiaowei Li, Yongtian Wang, Lingling Huang
Abstract
Metasurfaces are promising platforms for integrated compact optical systems. Traditional metasurface holography design algorithms are limited to information capacity due to finite spatial bandwidth production, which is insufficient for the growing demand for big data storage and encryption. Here, we propose and demonstrate deep learning empowered ultra-dense complex-amplitude holography using step-moving cascaded metasurfaces. Using deep learning artificial intelligence optimization strategy, the barriers of traditional algorithms can be conquered to meet diverse practical requirements. Two metasurfaces are cascaded to form the desired holography. One of them can move to switch the reconstruction images due to diffraction propagation accumulated during the cascaded path. The diffraction pattern from the first metasurface propagates at a different distance and meets with the second metasurface, reconstructing the target holographic reconstructions in the far-field. Such a technique can provide a new solution for multi-dimensional beam shaping, optical encryption, camouflage, integrated on-chip ultra-high-density storage, etc.