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

• URL: http://arxiv.org/abs/2607.05187v2
• Date: Tue, 07 Jul 2026 17:35:12 GMT
• Title: SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits
• Abstract summary: Digital circuit reliability is threatened by the combined effects of Bias Temperature Instability (BTI) and Process Variation (PV)<n>Traditional reliability analysis methods fail to scale efficiently for large designs, creating a critical bottleneck in design space exploration.<n>We propose SMART, a novel framework that integrates Machine Learning (ML) with Monte Carlo simulation to enable rapid, high-fidelity reliability analysis.
• Score: 0.6999740786886536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations [14.561544063927228]
  We propose a comprehensive methodology to adapt a deterministic physics foundation model into a highly efficient and accurate generative surrogate.<n>First, we introduce a two-stage inference scheme that combines a large-stride causal auto-regressive model with a non-causal temporal super-resolution network.<n>Second, we present Mean prediction regularization (MPR), a novel training objective that constrains the generative model to predict the deterministic distribution mean under null noise conditioning.
  arXiv  Detail & Related papers  (2026-08-06T14:41:11Z)
• Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization [0.0]
  Signal integrity (SI) analysis in printed circuit board interconnects faces increasing complexity.<n> Existing machine learning (ML) surrogate models for predicting SI metrics rely on fixed buffer parameters.<n>This paper introduces a buffer- parameterized ML surrogate modeling methodology capable of handling cross-technology variations without retraining.
  arXiv  Detail & Related papers  (2026-05-18T10:12:57Z)
• Dynamic Symmetric Point Tracking: Tackling Non-ideal Reference in Analog In-memory Training [46.75046795995564]
  We present the first theoretical characterization of the pulse complexity of SP calibration and the resulting estimation error.<n>We propose a dynamic SP estimation method that tracks the SP during model training, and establishes its convergence guarantees.
  arXiv  Detail & Related papers  (2026-02-24T19:41:34Z)
• KS-Net: Multi-layer network model for determining the rotor type from motor parameters in interior PMSMs [0.0]
  This study shows that the rotor shape of IPMSMs can be predicted with high accuracy using data-driven approaches.<n>The findings provide a solid foundation for accelerating motor design processes, developing automated rotor identification systems, and enabling data-driven fault diagnosis in engineering applications.
  arXiv  Detail & Related papers  (2025-10-17T14:32:05Z)
• AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning [0.0]
  Many real-world systems display intrinsic randomness, requiring simulators whose outputs are random variables.<n>While Monte Carlo methods can handle this, their high computational cost is often prohibitive.<n>This work introduces an active learning framework to further reduce the computational burden of reliability analysis using emulators.
  arXiv  Detail & Related papers  (2025-07-06T22:07:57Z)
• A Hybrid Artificial Intelligence Method for Estimating Flicker in Power Systems [42.76841620787673]
  This paper introduces a novel hybrid AI method combining H filtering and an adaptive linear neuron network for flicker component estimation in power distribution systems.<n>The proposed method leverages the robustness of the H filter to extract the voltage envelope under uncertain and noisy conditions followed by the use of ADALINE to accurately identify flicker frequencies embedded in the envelope.
  arXiv  Detail & Related papers  (2025-06-16T15:38:39Z)
• Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling [41.19330514054401]
  Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness.<n>We propose the Explicit Knowledge Boundary Modeling framework to integrate fast and slow reasoning systems to harmonize reliability and usability.
  arXiv  Detail & Related papers  (2025-03-04T03:16:02Z)
• Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures [0.0]
  This study introduces a hybrid machine learning-based scale-bridging framework for predicting the permeability of fibrous textile structures.<n>Four methodologies were evaluated: Single Scale Method (SSM), Simple Upscaling Method (SUM), Scale-Bridging Method (SBM), and Fully Resolved Model (FRM)
  arXiv  Detail & Related papers  (2025-02-07T16:09:25Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.<n>We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.<n>Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• Bisimulation metric for Model Predictive Control [44.301098448479195]
  Bisimulation Metric for Model Predictive Control (BS-MPC) is a novel approach that incorporates bisimulation metric loss in its objective function to directly optimize the encoder. BS-MPC improves training stability, robustness against input noise, and computational efficiency by reducing training time. We evaluate BS-MPC on both continuous control and image-based tasks from the DeepMind Control Suite.
  arXiv  Detail & Related papers  (2024-10-06T17:12:10Z)
• PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture [46.266960248570086]
  This study tackles the quadratic complexity of the self-attention mechanism by introducing a complexity local attention mechanism for effective feature aggregation. We also introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel. We show that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.
  arXiv  Detail & Related papers  (2024-08-10T10:16:03Z)
• Fast Distributionally Robust Learning with Variance Reduced Min-Max Optimization [85.84019017587477]
  Distributionally robust supervised learning is emerging as a key paradigm for building reliable machine learning systems for real-world applications. Existing algorithms for solving Wasserstein DRSL involve solving complex subproblems or fail to make use of gradients. We revisit Wasserstein DRSL through the lens of min-max optimization and derive scalable and efficiently implementable extra-gradient algorithms.
  arXiv  Detail & Related papers  (2021-04-27T16:56:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.