Traditionally, rule-based machine vision systems used with automated optical inspection (AOI) systems do not perform well. Detecting potential defects (NG) through deep learning can enhance the reliability of the inspection process. The AOI machine uses Cognex Deep Learning tools to identify potential NG situations and provide those images to the system. The defect detection tool can dynamically capture regions of interest, while the classification tool can categorize different types of defects, distinguishing between defective and acceptable wire bonds. Categorizing defects not only helps identify process issues to avoid costly rework downstream, but can also successfully identify defects at the micron level, improving IC chip yield and lifetime performance.