Root Cause Analysis (RCA) and Layout Pattern Analysis (LPA) are critical technologies for Diagnosis Driven Yield Learning in designing and manufacturing integrated circuits. Recent advancements of AI technologies can help improving yield learning accuracy and transferring the yield learning experiences from old designs to new designs or from old technologies to the new ones. In this talk, we share our experiences in this research area and discuss the following techniques: (1) A neural-network-based framework for RCA. The framework has a self-adaptive module that is able to adapt the inference module to new designs and new technologies based on a few new samples. (2) An encoder network framework for LPA. It applies Contrastive Learning to extract representations of layout snippets that are invariant to trivial transformations such as shift, rotation, and mirroring. The layout snippets are then clustered to form layout patterns. The causal relationship between any potential layout patterns and the systematic defects is identified by the Causal Representation Learning. (3) An unsupervised learning framework by using a Deep Latent Variable model consisting of a probabilistic encoder and a regularization decoder. The encoder transforms the features from diagnosis reports to latent variables characterizing the root cause distribution. The regularization decoder uses a Graph Attention Network to represent the mapping from the true root causes to suspicious root causes reported.