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Efficient high-dimensional performance modeling of nanoscale analog and mixed signal (AMS) circuits is extremely challenging. In this paper, we propose a novel structure-aware modeling (SAM) technique. The key idea of SAM is to accurately solve the model coefficients by applying an efficient statistical algorithm to exploit the underlying structure of AMS circuits. As a result, SAM dramatically reduces...
Post-silicon tuning has recently emerged as an important technique to combat large-scale uncertainties (e.g., process variation, device modeling errors, etc) for today's nanoscale circuits. This talk presents a novel Bayesian Model Fusion (BMF) technique for efficient post-silicon performance modeling and tuning of analog and mixed-signal (AMS) circuits. The key idea is to borrow the simulation or...
While statistical analysis has been considered as an important tool for nanoscale integrated circuit design, many IC designers would like to know the design-specific worst-case corners for circuit debugging and failure diagnosis. In this paper, we propose a novel algorithm to efficiently extract the worst-case corners for nanoscale ICs. Our proposed approach mathematically formulates a quadratically...
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...
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