Non-destructive analysis of IC bond pad structures using signal processing of acoustic emission signatures and finite element analysis

Increased risk of crack formation in the brittle insulating layers of crack-sensitive backend-of-line (BEOL) structures in semiconductor integrated circuits during wafer probing or wire bonding process is detrimental to product reliability. This work presents a high-resolution, in-situ material testing system that integrates acoustic emission (AE) testing with a nanoindentation system, as well as the finite element analysis (FEA) simulation method, for faster characterization of BEOL structures to optimize the reliability of wafer probing and wire bonding processes by detection and monitoring crack generation in thin film stack structures. This hybrid testing system is used to determine the critical load of thin film stack structures with Al-Cu input/output (I/O) pads, insulating layers, and the embedded Cu layers. Cracks were induced during the nanoindentation, scanning electron microscopy (SEM), and load-displacement curve analysis were used to confirm the formation and propagation of cracks in the insulating layers below the I/O pad. In order to improve the manual classification performance and understand the physical meaning of AE signals, this work introduces a machine learning based signal processing approach based on a k-means clustering algorithm applied to 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. 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. Experimentation alone is not sufficient to study insulation layer failure in metal-topped thin film stack structures because of the limitation of large test parameters of wafer structures due to the cost, resources, and lack of methods to obtain the critical stresses and deformation of testing samples. Finite element analysis (FEA) simulation was carried out to evaluate the contact-related stress distribution and obtain the critical stress by replicating the nanoindentation process to understand the critical conditions that lead to brittle fracture in insulating layers. Experimental results are used to validate the simulation model by the correlation between the location of highest stress obtained in simulation and the crack location in SEM images. Simulation results are used to complement and explore beyond the experimental results. Multiple thin film stack structures with different layer thicknesses are tested by experiment and correlated with simulation in parameter studies. Different tip materials are simulated to explore beyond the experiment

Keywords: Mechanical Engineering.
Advisor: Gu Wendy (Professor of mechanical engineering)
Senesky Debbie
Cai Wei