Cluster-based acoustic emission signal processing and loading rate effects study of nanoindentation on thin film stack structures. (15th February 2022)

• Record Type: Journal Article
• Authors: Liu, Chen; Nagler, Oliver; Tremmel, Florian; Unterreitmeier, Marianne; Frick, Jessica J.; Patil, Radhika P.; Gu, X. Wendy; Senesky, Debbie G.
• Is Part Of: Mechanical systems and signal processing. Volume 165(2022)
• Publication Date: 2022-02-15
• Subjects: Loading rate effects -- Acoustic emission (AE) -- Nanoindentation -- Data clustering -- K-means -- Autoencoder; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108301
• Languages: English
• ISSNs: 0888-3270

Abstract

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