Aging Mechanisms and AI-Based Lifetime Prediction of Integrated Circuits

Abstract

Modern electronics use integrated circuits as their fundamental component to enable operations of autonomous vehicles, high-performance servers, and edge AI devices. The chips experience degradation through time because of stress-induced aging mechanisms, including Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), and Electromigration (EM). Accurate prediction of chip lifespan stands as a strategic necessity for long-term deployment because reliability has become the main bottleneck. This paper investigates the physical mechanisms of chip aging while examining how artificial intelligence techniques predict device operational lifespan. A research examines conventional forecasting approaches starting with High Temperature Operating Life (HTOL) assessment and a physics-of-failure analysis. It compares them to recent deep learning models with Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) implementation systems. The paper presents the effectiveness of the AI model through engineering cases and the latest research data by showing its ability to track nonlinear age progression under different operational settings. LSTM-based frameworks achieve superior accuracy, speed, and adaptability according to results from the literature after receiving appropriate time-series sensor data training. The research demonstrates that predictive maintenance and reliability management will become more data-based, which leads to better lifecycle decisions for essential electronic systems.