J. Ross Thomson

Statistical modeling of MOS devices for parametric yield prediction

Abstract In the manufacturing of VLSI circuits, engineering designs should take into consideration random variations arising from processing. In this paper, statistical modeling of MOS devices is reviewed, and effective and practical models are developed to predict the performance spread (i.e., parametric yield) of MOS devices and circuits due to the process variations. To illustrate their applications, the models are applied to the 0.25 μm CMOS technology, and measured data are included in support of the model calculations.

Worst-case analysis and statistical simulation of MOSFET devices based on parametric test data

Abstract A practical and efficient approach for estimating the MOSFET device and circuit performance distributions is presented. The proposed method is based on the Latin hypercube sampling technique and direct extracting and utilizing the statistical information obtained from a population of parametric test data. Using this approach, a set of worst-case models taking into account data correlations and equal probability constraints is developed. The procedure allows for a systematical and accurate way to predict the performance spread and worst case of MOSFET circuits, as well as a greatly reduced computation time for statistical simulation. Measured data of two digital circuits are included in support of the modeling work.