Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the

Artificial Intelligence – deep learning is a subset of machine learning. The man who coined the phrase'machine leaning', IBM developer Arthur Samuel, once described it as a "field of study that gives computers the ability to learn without being explicitly programmed." At the time he was teaching IBM's systems to play checkers… Where machine learning algorithms work well on datasets that have up to a few hundred columns, unstructured datasets like images or videos have so many features that traditional methods of training them are unfeasible. As Datarobot explains: "Deep learning algorithms learn progressively more about the image as it goes through each neural network layer. Early layers learn how to detect low-level features like edges, and subsequent layers combine features from earlier layers into a more holistic representation. For example, a middle layer might identify edges to detect parts of an object in the photo such as a leg or a branch, while a deep layer will detect the full object such as a dog or a tree."

Artificial Intelligence – deep learning is a subset of machine learning. The man who coined the phrase'machine leaning', IBM developer Arthur Samuel, once described it as a "field of study that gives computers the ability to learn without being explicitly programmed." At the time he was teaching IBM's systems to play checkers… Where machine learning algorithms work well on datasets that have up to a few hundred columns, unstructured datasets like images or videos have so many features that traditional methods of training them are unfeasible.