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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是改善扫描电子显微镜（SEM）图像采集。 SEM在几乎所有需要表征固体材料的领域都至关重要，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SEM发展的驱动因素，但最近，光束尺寸的限制使得进一步发展变得越来越昂贵。 该项目正在开发的方法有可能提高分辨率和/或增加吞吐量，不需要昂贵的硬件改造，在各种领域和应用中都是有价值的，并使电子显微镜更便宜，更广泛的应用。这个小企业创新研究第一阶段项目旨在开发一种图像重建产品，它可以提高所拍摄图像的分辨率，通过扫描电子显微镜（SEM）或实现更快的高分辨率图像采集。 该创新建立在医学成像、监控和天文学等其他领域的发展基础上，在存在噪声的情况下执行基于软件的图像恢复（反卷积）。 这项工作的关键是开发一种算法来确定撞击样品的探针中电子的空间分布（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上的界限，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.