Name
Title | ||
|---|---|---|
Finding Deterministic Solution From Underdetermined Equation: Large-Scale Performance Variability Modeling of Analog/RF Circuits |
Abstract | ||
|---|---|---|
The aggressive scaling of integrated circuit technology results in high-dimensional, strongly-nonlinear performance variability that cannot be efficiently captured by traditional modeling techniques. In this paper, we adapt a novel L0-norm regularization method to address this modeling challenge. Our goal is to solve a large number of (e.g., 104-106) model coefficients from a small set of (e.g., 102-103) sampling points without over-fitting. This is facilitated by exploiting the underlying sparsity of model coefficients. Namely, although numerous basis functions are needed to span the high-dimensional, strongly-nonlinear variation space, only a few of them play an important role for a given performance of interest. An efficient orthogonal matching pursuit (OMP) algorithm is applied to automatically select these important basis functions based on a limited number of simulation samples. Several circuit examples designed in a commercial 65 nm process demonstrate that OMP achieves up to 25× speedup compared to the traditional least-squares fitting method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TCAD.2010.2061292 | IEEE Trans. on CAD of Integrated Circuits and Systems |
Keywords | DocType | Volume |
process variation,important basis function,integrated circuit technology,novel l0-norm regularization method,high-dimensional strongly-nonlinear variation space,deterministic solution,OMP algorithm,important role,integrated circuit modelling,analogue integrated circuits,size 65 nm,model coefficient,least-square fitting method,underdetermined equation,rf circuit,Integrated circuit,radiofrequency integrated circuits,large number,integrated circuit technology result,performance modeling,large-scale performance variability modeling,L0-norm regularization method,circuit example,analog-RF circuits,fitting method,orthogonal matching pursuit algorithm,iterative methods,modeling challenge,limited number | Journal | 29 |
Issue | ISSN | Citations |
11 | 0278-0070 | 15 |
PageRank | References | Authors |
0.76 | 14 | 1 |