SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits
2026-07-06 • Machine Learning
Machine LearningHardware Architecture
AI summaryⓘ
The authors focus on improving how we predict the reliability of tiny digital circuits that can fail due to certain physical effects. They created a new method called SMART that uses machine learning combined with simulations to quickly and accurately estimate how circuit components will behave over time. Instead of slow traditional methods, their approach speeds up analysis by about 95% while keeping errors very low. They tested this on standard circuit examples and showed it can help design more reliable digital systems efficiently.
CMOS technologyBias Temperature Instability (BTI)Process Variation (PV)Digital circuit reliabilityMonte Carlo simulationRandom Forest regressionBayesian OptimizationISCAS85 benchmarkMachine LearningHyperparameter tuning
Authors
Arash Esshaghi, Siavash Es'haghi, Gholamreza Shahabadi, Alireza Moradi
Abstract
As CMOS technology scales into the deep nanometer regime, digital circuit reliability is increasingly threatened by the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV). Traditional reliability analysis methods, which rely on computationally intensive simulations or extensive lookup tables, fail to scale efficiently for large designs, creating a critical bottleneck in design space exploration. To address this, we propose SMART, a novel framework that integrates Machine Learning (ML) with Monte Carlo simulation to enable rapid, high-fidelity reliability analysis. SMART employs Random Forest regression to predict gate delay distributions directly, bypassing time-consuming atomic model parameter extractions. Crucially, the model utilizes Bayesian Optimization for automated hyperparameter tuning, ensuring maximum predictive robustness across diverse libraries. Experimental validation on ISCAS85 benchmark circuits demonstrates that SMART achieves a 94.54% reduction in analysis time compared to state-of-the-art methods, while maintaining a remarkable average accuracy error of just 1.63%. By shifting computational complexity to an offline training phase, the proposed framework offers a scalable, accurate solution for designing resilient, reliability-aware digital systems.