SMART A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits

Jul 16, 2026

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As CMOS technology scales into deep-nanometer nodes, digital circuit reliability is increasingly endangered by stochastic Bias Temperature Instability (BTI) and Process Variation (PV). Conventional reliability workflows rely on costly simulations or extensive lookup tables, limiting scalability for large designs and slowing design-space exploration. SMART integrates Machine Learning with Monte Carlo to deliver rapid, high-fidelity analysis by predicting gate-delay distributions using Random Forest regression and tuning hyperparameters via Bayesian Optimization. Experiments on ISCAS85 show a 94.54% runtime reduction with 1.63% average accuracy error.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Introduction

Bayesian Optimization for Hyperparameter Tuning

SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits Arash Esshaghi1, Siavash Es'haghi2*, Gholamreza Shahabadi3, Alireza Moradi4 1 Department of Optimization and Mathematics, Payame Noor University, Tehran, Iran. ; [arash.esshaghi@gmail.com](arash.esshaghi@gmail.com) 2 Department of Electrical and Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran. ; [s_esshaghi@iau.ac.ir](s_esshaghi@iau.ac.ir) 3 Department of Electrical Engineering, National University of Skills (NUS), Tehran, Iran; [ghr.shahabadi@gmail.com](ghr.shahabadi@gmail.com) 4 Department of Electrical Engineering, Se.C, Islamic Azad University, Semnan, Iran; [Alireza.moradi@iau.ac.ir](Alireza.moradi@iau.ac.ir) * Corresponding Author: [s_esshaghi@iau.ac.ir](s_esshaghi@iau.ac.ir) 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. Keywords: Reliability, Stochastic aging, Process variation, NBTI, Machine learning, Monte Carlo, Automated hyperparameter tuning 1. Introduction The continuing scaling of CMOS technology into the nanometer regime has enabled unprecedented levels of integration and performance in digital systems. However, this advancement comes at the cost of heightened reliability challenges, primarily due to transistor aging and process variation (PV) in nanoscale CMOS circuits [1, 2] . Transistor aging, a temporal phenomenon, manifests as gradual degradation of transistor characteristics, notably the threshold voltage, leading to increased gate delays, timing violations, and eventual circuit failure [3] . Among the aging mechanisms, Bias Temperature Instability (BTI), encompassing Negative BTI (NBTI) in PMOS transistors and Positive BTI (PBTI) in NMOS transistors, is particularly detrimental due to its pronounced impact on advanced technology nodes [4, 5] . Concurrently, process variation, a spatial effect arising from manufacturing non-idealities such as lithography imperfections, introduces stochastic deviations in transistor parameters, adversely affecting circuit yield and performance immediately after fabrication [6, 7] . The stochastic nature of both PV and BTI in sub-45nm technologies exacerbates these challenges, necessitating robust analysis methods to ensure circuit reliability and lifetime. The simultaneous analysis of stochastic aging and PV (APV) is critical for modern digital circuit design. These phenomena directly impact key metrics such as yield, performance, and operational lifetime, which are important for applications ranging from consumer electronics to automotive systems. For instance, PV can reduce fabrication yield by causing deviations in tr

FAQs

What reliability threats are addressed by SMART in modern CMOS circuits?

SMART targets the combined stochastic effects of Bias Temperature Instability (BTI) and Process Variation (PV), which degrade transistor behavior and impact timing, yield, and lifetime.

How does SMART accelerate reliability analysis compared with traditional approaches?

SMART replaces time-consuming atomic-parameter extraction and costly simulations with Machine Learning prediction of gate-delay distributions, while still leveraging Monte Carlo in the framework to maintain high fidelity.

Why is Bayesian Optimization used in SMART?

Bayesian Optimization automates Random Forest hyperparameter tuning to maximize predictive robustness across diverse libraries, improving accuracy without exhaustive manual search.

Jul 16, 2026

Upload by Liam