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In this paper, we propose a novel multi-mode/multi-corner sparse regression (MSR) algorithm to build large-scale performance models of integrated circuits at multiple working modes and environmental corners. Our goal is to efficiently extract multiple performance models to cover different modes/corners with a small number of simulation samples. To this end, an efficient Bayesian inference with shared...
A system-level statistical analysis methodology is described that captures the impact of inter- and intra-die process variations for read timing failures in SRAM circuit blocks. Unlike existing approaches that focus on cell-level performance metrics for isolated sub-components or ignore inter-die variability, the system-level performance is accurately predicted for the entire SRAM circuit that is...
This paper proposes a new approach to analyze crosstalk of coupled interconnects in the presence of process variations. The suggested method translates correlated process variations into orthogonal random variables by principle component analysis (PCA). combined with polynomial chaos expression (PCE), the technique utilizes Stochastic Collocation Method (SCM) to analyze the system response of coupled...
The continuous technology scaling brings about high-dimensional performance variations that cannot be easily captured by the traditional response surface modeling. In this paper we propose a new statistical regression (STAR) technique that applies a novel strategy to address this high dimensionality issue. Unlike most traditional response surface modeling techniques that solve model coefficients from...
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