论文标题
无斑点全息光刻的相位概率塑形
Phase-probability shaping for speckle-free holographic lithography
论文作者
论文摘要
自1948年发明以来，光学全息图已经经历了快速发展，但是由于消除不规则的复杂场叠加中固有波动的基本困难，随机分布强度的伴随斑点现在仍未受到损害。尽管空间，时间和频谱平均减少了斑点，但通过全息图重建高均匀性，边缘和形状无限图像是极具挑战性的。在这里，我们预测可以通过缩小编码相的概率密度分布以使光学叠加的概率密度分布来清除。在这种物理洞察力的指导下，开发了机器学习辅助的概率成形（MAPS）方法，以禁止计算机生成的全息图（CGH）中强度的波动，从而赋予了不规则的不规则图像的实验性重建，以超级斑点对比（c = 0.08）和记录型优势（C = 0.08）和效果（C = 0.08）和锋利的尖锐（C = 0.08）和1000 000000千MM。它打破了展示高端CGH光刻的最终障碍，从而使我们能够成功地模拟任意形状和边缘呈现的结构，例如涡旋光栅和二维随机条形码。
Optical holography has undergone rapid development since its invention in 1948, but the accompanying speckles with randomly distributed intensity are still untamed now due to the fundamental difficulty of eliminating intrinsic fluctuations from irregular complex-field superposition. Despite spatial, temporal and spectral averages for speckle reduction, it is extremely challenging to reconstruct high-homogeneity, edge-sharp and shape-unlimited images via holography. Here we predict that holographic speckles can be removed by narrowing the probability density distribution of encoded phase to homogenize optical superposition. Guided by this physical insight, a machine-learning-assisted probability-shaping (MAPS) method is developed to prohibit the fluctuations of intensity in a computer-generated hologram (CGH), which empowers the experimental reconstruction of irregular images with ultralow speckle contrast (C=0.08) and record-high edge sharpness (~1000 mm-1). It breaks the ultimate barrier of demonstrating high-end CGH lithography, thus enabling us to successfully pattern arbitrary-shape and edge-sharp structures such as vortex gratings and two-dimensional random barcodes.
