Efficient Discharge Waveform Distribution Measurement Using Active Machine Learning

Abstract

Near-field scanning (NFS) is a promising method to capture the current propagation in an electronic system through an automated scanning system. This article presents a novel and efficient measurement method for discharge waveform distribution based on active machine learning using NFS. Implicitly, the query-by-committee (QBC) active learning method is adopted to select scanning points with high uncertainty. The proposed approach is computationally efficient in real-time NFS, demonstrates higher reconstruction accuracy than random sampling using the same amount of sparse samples, and is much more efficient than full scanning.