Abstract
This paper presents a novel method of restoring the electron beam (EB) measurements that are degraded by linear motion blur. This is based on a fuzzy inference system (FIS) and Wiener inverse filter, together providing autonomy, reliability, flexibility, and real-time execution. This system is capable of restoring highly degraded signals without requiring the exact knowledge of EB probe size. The FIS is formed of three inputs, eight fuzzy rules, and one output. The FIS is responsible for monitoring the restoration results, grading their validity, and choosing the one that yields to a better grade. These grades are produced autonomously by analyzing results of a Wiener inverse filter. To benchmark the performance of the system, ground truth signals obtained using an 18 μm wire probe were compared with the restorations. Main aims are therefore: (a) Provide unsupervised deblurring for device independent EB measurement; (b) improve the reliability of the process; and (c) apply deblurring without knowing the probe size. These further facilitate the deployment and manufacturing of EB probes as well as facilitate accurate and probe-independent EB characterization. This paper’s findings also makes restoration of previously collected EB measurements easier where the probe sizes are not known nor recorded.
Highlights
The main goal of fuzzy systems is to define and control sophisticated processes by incorporating and taking advantage of human knowledge and experience
This article proposes a Wiener filter that is monitored by a fuzzy inference system
This fuzzy inference system (FIS) is comprised of three crisp inputs that included the point spread function (PSF) length or probe size (Lh) deviation, attenuation of the vanishing frequencies, and deconvolution residue
Summary
The main goal of fuzzy systems is to define and control sophisticated processes by incorporating and taking advantage of human knowledge and experience. Where in the spatial domain, f , g, h, and n are the ground truth signal (EB distribution) of length L f , degraded signal (measurement from probe), point spread function (PSF) of length Lh, and noise respectively Their frequency domains are represented by uppercase letters F, G, and H. Utilizing deblurring techniques for industrial purposes requires real-time, reliable, and unsupervised methods To satisfy these requirements, this article proposes a Wiener filter that is monitored by a fuzzy inference system. The fuzzy inference system deals with the uncertainty of the deconvolution by monitoring the entire restoration process This FIS is comprised of three crisp inputs that included the PSF length or probe size (Lh) deviation, attenuation of the vanishing frequencies, and deconvolution residue. The values of membership functions parameters are provided and a comparison is made between implementing the fuzzy system with and without the knowledge of probe size (Lh)
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