A Fuzzy Inference System for Unsupervised Deblurring of Motion Blur in Electron Beam Calibration

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 m 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.