论文标题
人工智能启用的子波长对象的光学计量学
Optical Metrology of Sub-Wavelength Objects Enabled by Artificial Intelligence
论文作者
论文摘要
几十年来，显微镜和各种形式的干涉仪已在通常大于光λ波长的对象的光学计量学中使用。然而，由于衍射极限，亚波长对象的计量学被认为是不可能的。我们报告说，通过分析具有深度学习分析的对象散射的相干光的衍射模式，对次波长对象的物理大小超过λ/800的测量。使用633nm激光器，我们表明可以以0.77nm的精度测量不透明屏幕中的子波长缝隙的宽度，从而挑战了电子束和离子束光刻的准确性。该技术适用于具有集成计量和处理工具的智能制造应用中纳米尺寸的高速非接触量测量。
Microscopes and various forms of interferometers have been used for decades in optical metrology of objects that are typically larger than the wavelength of light λ. However, metrology of subwavelength objects was deemed impossible due to the diffraction limit. We report that measurement of the physical size of sub-wavelength objects with accuracy exceeding λ/800 by analyzing the diffraction pattern of coherent light scattered by the objects with deep learning enabled analysis. With a 633nm laser, we show that the width of sub-wavelength slits in opaque screen can be measured with accuracy of 0.77nm, challenging the accuracy of electron beam and ion beam lithographies. The technique is suitable for high-rate non-contact measurements of nanometric sizes in smart manufacturing applications with integrated metrology and processing tools.
