Advances in manufacturing process technology are key ensembles for the production of
integrated circuits in the sub-micrometer region. It is of paramount importance to assess the …

Hierarchical uncertainty quantification can reduce the computational cost of stochastic circuit
simulation by employing spectral methods at different levels. This paper presents an efficient …

Fabrication process variations are a major source of yield degradation in the nanoscale
design of integrated circuits (ICs), microelectromechanical systems (MEMSs), and photonic …

Stochastic spectral methods have achieved a great success in the uncertainty quantification
of many engineering problems, including variation-aware electronic and photonic design …

As new services and business models are being associated with the power distribution
network, it becomes of great importance to include load uncertainty in predictive …

Process variations can significantly degrade device performance and chip yield in silicon
photonics. In order to reduce the design and production costs, it is highly desirable to predict …

Many critical electronic design automation (EDA) problems suffer from the curse of
dimensionality, ie, the very fast-scaling computational burden produced by large number of …

This paper presents a systematic approach for the statistical simulation of nonlinear
networks with uncertain circuit elements. The proposed technique is based on spectral …

This paper presents hybrid automaton modeling, comparative model validation, and formal
verification of stability through reachability analysis of pulse width modulation (PWM) dc-dc …

In this brief, we compare three polynomial chaos expansion (PCE)-based methods for
ANCOVA (ANalysis of COVAriance) indices based global sensitivity analysis for correlated …