arXiv AI

Dual-domain fused LSTM modeling for efficient time-dependent reliability analysis

arXiv:2607. 18291v1 Announce Type: cross Abstract: Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties.

arXiv Machine Learning
Jul 7

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

arXiv:2607. 05187v1 Announce Type: new 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).

By Arash Esshaghi, Siavash Es'haghi, Gholamreza Shahabadi, Alireza Moradi
Hugging Face Trending Papers
Jul 6

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

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.

arXiv Machine Learning
Jun 16

Graphical conditional generative modeling for digital twin modeling

arXiv:2606. 16219v1 Announce Type: cross Abstract: Digital twin modeling, including control and data assimilation under model uncertainty, often faces an open-ended fidelity problem: adding variables, data streams, and time scales can indefinitely increase model complexity, ultimately producing systems that are difficult to maintain, validate, interpret, and use for stress or safety testing.

By Zongren Zou, Th\'eo Bourdais, Ricardo Baptista, Houman Owhadi