A Hybrid Finite Element Analysis–Machine Learning–Generative Artificial Intelligence (Generative Adversarial Networks) Framework for Predicting Reliability Risks in Three-Dimensional Integrated Circuit Packaging

Abstract The rapid rise in power density for modern artificial intelligence (AI) and data-center hardware has made thermal management and long-term reliability major challenges in advanced electronics. Ensuring high availability requires predictive tools capable of estimating component lifespan under continuously changing operating conditions. Three-dimensional integrated circuit (3D IC) technology enables high functional density (Rahangdale et al., 2017, “Structural Integrity Optimization of 3D TSV Package by Analyzing Crack Behavior at TSV and BEOL,” 2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC), Saratoga Springs, NY, pp. 201--208), but predicting its lifetime under realistic workloads remains difficult. Prior studies have examined individual failure mechanisms, such as solder joint fatigue, yet a fast and comprehensive prognostic reliability framework is still missing. This work proposes a two-stage methodology that integrates physics-based simulations with machine learning (ML) and conditional generative adversarial network (cGAN) models to accelerate reliability prediction for 3D IC structures. First, detailed thermal finite element analysis (FEA) was conducted using synthetic thermal and thermomechanical load profiles derived from real data-center behavior. These simulations capture temperature distribution across the stacked dies under varying power and cooling conditions. A subsequent structural FEA extracts key reliability indicators, such as solder joint plastic strain and stress. Hundreds of simulations form the dataset for training ML regression models that rapidly predict thermal and mechanical metrics, serving as surrogate models for expensive FEA runs. The second component introduces an ML-based visual prognosis method. FEA-generated thermal contour images were used to train a cGAN that can instantly produce FEA-like thermal maps. This provides real-time, intuitive insight into thermal behavior and stress concentration. By replacing slow simulations with fast ML predictions and images, the framework supports continuous reliability monitoring.