Deep Topological Data Analysis for High-Dimensional IC Data: Architectures, Algorithms, and Applications

High-dimensional and nonlinear IC design and manufacturing data present fundamental challenges to traditional modeling and signal-analysis techniques. This lecture introduces Deep Topological Data Analysis (DTDA), a hybrid computational framework combining persistent homology, deep learning, and statistical inference for semiconductor data mining at scale. We will explore mathematical foundations alongside practical deployment examples in yield drift detection, defect pattern discovery, reticle/hotspot classification, lithography layout inference, and metrology/inspection analytics. The talk highlights architectural design choices, algorithmic complexity, and model-to-silicon interpretability. Attendees will gain an understanding of how DTDA enhances IC systems insight, optimization, and reliability across circuits, processes, and design flows.