TSV Defects Classification with Machine Learning Approaches

Abstract

The S parameter amplitude, latency, resistance, and inductance of TSV-RDL structures with the presence of five kinds of defects are simulated as feature vectors for defect detection and classification. Three nondestructive defect classification schemes for the TSV-RDL structure in advanced packaging are evaluated. Feedforward neural network with rectified linear unit activation function for the backpropagation algorithm is superior for defect classification and may play an important role in design for test and build-in self-repair circuit design.