Large-scale analog/RF performance modeling by statistical regression

In this paper, we introduce several large-scale modeling techniques to analyze the high-dimensional, strongly-nonlinear performance variability observed in nanoscale manufacturing technologies. 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. Our circuit example designed in a commercial 65 nm process demonstrates that the proposed techniques achieve 25× speedup compared with the traditional response surface modeling.