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.
