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This investigation utilizes a material testing system that integrates acoustic emission (AE) testing with a nanoindentation system for crack generation and detection in Al-Cu top thin-film stack structures. The suitability of using the AE method was verified with scanning electron microscope (SEM) images of indent cross-sections. In order to cluster the AE signals based on a different physical meaning, a signal processing approach based on the Gaussian mixture model (GMM) clustering algorithm was applied. Principal component analysis (PCA) and autoencoder feature extraction methods were used to reduce the dimension of the signal. This signal processing approach has the promising ability to distinguish AE events associated with crack formation and metal layer plastic deformation. This integrated test system and signal processing approach provide a high-resolution mechanical testing platform for studying and enabling automatic, non-destructive crack detection in wafer probing.

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.