SBIR Phase I: Improving Scanning Electron Microscope Performance Through Advanced Image Restoration Technology

小型企业创新研究(SBIR)第一阶段项目的更广泛影响/商业潜力是改进扫描电子显微镜(SEM)图像采集。扫描电子显微镜在几乎所有需要对固体材料进行表征的领域都是至关重要的，特别是半导体和电子制造、纳米技术和生命科学。分辨率和减小光束尺寸几乎一直是扫描电子显微镜发展的驱动因素，但最近，光束尺寸的限制使进一步的进展变得越来越昂贵。该项目正在开发的方法具有提高分辨率和/或增加吞吐量的潜力，而无需昂贵的硬件修改将在各种领域和应用中具有价值，并使电子显微镜更实惠和更广泛的应用。这个小型企业创新研究第一阶段项目旨在开发一种图像重建产品，该产品将提高扫描电子显微镜(SEM)拍摄的图像的分辨率或实现更快、更高分辨率的图像采集。这项创新建立在医学成像、监视和天文学等其他领域的发展基础上，可以在存在噪声的情况下进行基于软件的图像恢复(去卷积)。这项工作的关键是开发一种算法来确定撞击样品的探测器中电子的空间分布(点扩散函数)。本项目的目标是开发合适的参考资料，探索这种方法在低端中小企业中的边界，并评估这种方法对高端中小企业的价值主张。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve scanning electron microscopy (SEM) image acquisition. SEM is critical in nearly all fields that require characterization of solid materials, in particular, semiconductor and electronics manufacturing, nanotechnology and life sciences. Resolution, and reducing beam size, has almost always been the driving factor in SEM development, but recently, the limits of beam size are making further progress increasingly expensive. The approach being developed in this project has the potential to enhance resolution, and/or increase throughput, without expensive hardware modifications would be valuable in a variety of fields and application and make electron microscopy more affordable and accessible for a wider range of applications.This Small Business Innovation Research Phase I project aims to develop an image reconstruction product that will either improve the resolution of an image taken by a scanning electron microscope (SEM) or enable faster, high resolution image acquisition. The innovation builds on developments in other fields such as medical imaging, surveillance and astronomy to perform software-based image restoration (deconvolution) in the presence of noise. Critical to this effort is the development of an algorithm to determine the spatial distribution of the electrons in the probe striking a sample (the point spread function). The objectives of this project are to develop suitable references, explore the boundaries of this approach on low-end SEMs and evaluate the value proposition of the approach for high-end SEMs.