Abstract
Abstract This chapter assesses the potential impact of neural networks on package-level failure analysis, the challenges presented by next-generation semiconductor packages, and the measures that can be taken to maximize FA equipment uptime and throughput. It presents examples showing how neural networks have been trained to detect and classify PCB defects, improve signal-to-noise ratios in SEM images, recognize wafer failure patterns, and predict failure modes. It explains how new packaging strategies, particularly stacking and disintegration, complicate fault isolation and evaluates the ability of various imaging methods to locate defects in die stacks. It also presents best practices for sample preparation, inspection, and navigation and offers suggestions for improving the reliability and service life of tools.
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