使用深度学习突破光学信息存储的极限
Pushing the limits of optical information storage using deep learning
摘要：
衍射极大地限制了光数据存储中的位密度。为了增加存储密度，必须探索涉及补充记录尺寸和鲁棒读出方案的替代策略。在这里，我们建议在亚波长介电纳米结构的几何形状中编码多个比特的信息。高密度信息存储概念中的一个关键问题是相对于制造误差和实验噪声的信息读出的鲁棒性。使用基于机器学习的方法，其中散射光谱分析的人工神经网络，我们实现了准无错误读出的序列高达9位，编码在自上而下制造的硅纳米结构。我们证明，探测几个波长，而不是整个光谱是足够的强大的信息检索和读出可以进一步简化，利用显微图像的RGB值。我们的工作为使用平面硅纳米结构的高密度光学信息存储铺平了道路，与大规模生产的CMOS技术兼容。
英文摘要：
Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust read-out schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning based approach in which the scattering spectra are analyzed by an artificial neural network, we achieve quasi error free read-out of sequences of up to 9 bit, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production ready CMOS technology.
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