Cluster-based acoustic emission signal processing and loading rate effects study of nanoindentation on thin film stack structures

This paper presents a high-resolution, in-situ material testing system that integrates acoustic emission (AE) with nanoindentation for crack generation and detection in thin film stack structures. is used to find the critical contact load during wafer probing of crack-sensitive backend-of-line (BEOL) structures semiconductor integrated circuits. Scanning electron microscopy (SEM) load–displacement curve analysis were confirm formation propagation cracks multilayer In order improve manual classification performance understand physical meaning AE signals, this introduces machine learning based signal processing approach on k-means clustering algorithm applied collected signals. To obtain optimal number clusters, Davies–Bouldin, Dunn, Silhouette indices calculated, individual ratings cumulated voting scheme. Multiple feature extraction methods, including raw time-domain conventional extracted parameters, short-term energy, representation features learned by autoencoder, evaluated manually labeled clusters binary confusion matrices. A supervised technique, k-nearest neighbors algorithm, was also utilized different datasets using loading rates further investigate damage processes The influences signals have been investigated, rate effects observed – higher led loads. test provides high-resolution mechanical platform studying enabling automatic probing.