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This book delivers a focused, technical exploration of automated analog and RF integrated circuit sizing under process, voltage, and temperature variations, guiding readers through foundational concepts, current methodologies, and advanced machine-learning-driven approaches. It first examines multiple reinforcement-learning-based strategies for embedding PVT conditions directly into modern sizing flows, clarifying their conceptual differences and practical implications. It then explores a complementary deep-learning-assisted approach that leverages ANN-based performance regressors, transfer learning, and adaptive refinement to accelerate simulation-driven optimization without requiring extensive corner-specific datasets. Together, these chapters provide a grounded overview of current techniques and ongoing developments in automated analog IC design.