Artificial Intelligence-Driven Optimization for 3-D Integrated Circuit Manufacturing: A System of Systems Framework

Abstract

This article introduces an artificial intelligence (AI)-driven framework for optimizing 3-D integrated circuit (3D-IC) manufacturing through a system of systems (SoS) approach. Our framework integrates defect detection, process optimization, and electrical failure prediction using advanced methodologies, notably convolutional neural networks (CNNs), Random Forest classifiers, and long short-term memory (LSTM) networks. By dynamically aligning subsystem outputs with global manufacturing objectives, our framework addresses key challenges in through-silicon via (TSV) formation, defect reduction, and yield enhancement. Adaptive optimization techniques—including simulated annealing and dual annealing—are employed to refine critical parameters such as TSV depth, deposition rate, and etching temperature. Achieving a global yield of 58.48%, the proposed approach demonstrates its scalability and effectiveness in reducing defect rates while ensuring high manufacturing reliability. This article establishes a foundation for advancing AI-driven decision-making in complex manufacturing systems, bridging theoretical innovations and practical implementation.