ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR PREDICTING RELIABILITY IN ULTRA-LARGE-SCALE INTEGRATED CIRCUITS

As Ultra Large-scale Integration (ULSI) technology allows for the assembly of billions of transistors on a single chip; it has emerged as the fundamental component of modern machines and embedded systems. Application-specific integrated circuits, memory devices, and microprocessors are all being revolutionized through this advancement, leading to systems that are smaller, faster, and consume less energy. The long-term performance of ULSI systems has been jeopardized by serious reliability issues triggered by the fast scaling of devices into nanoscale levels. Time-dependent dielectric breakdown (TDDB), hot carrier injection (HCI), and negative bias temperature instability (NBTI) are instances of electrical degradation mechanisms that drastically diminish circuits' operating lifetimes. Nonlinear and dynamic deterioration patterns at the nanoscale are frequently ignored by conventional reliability evaluation techniques that rely on statistical distributions or physics-of-failure models. In order to enhance the dependability of ULSI circuits, the study proposed a predictive modeling framework based on machine learning. Using MOSRA-enabled HSPICE simulations of benchmark circuits under stress, reliability data were acquired. Maximum Likelihood Estimation (MLE) and Least Squares Estimation (LSE) have been applied for statistical preprocessing in order to organize the deterioration data. We trained and assessed the prediction accuracy of several ML algorithms, such as Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). The findings reveal that ML-driven models, in particular ANFIS, perform better than conventional reliability prediction techniques, as they can accurately simulate nonlinear behaviors and adapt to various stress circumstances. Through introducing predictive reliability techniques, our effort assists in improving the design of next-generation ULSI systems, extending their lifespan proactively, and detecting faults early.