Robust spatial correlation extraction with limited sample via L1-norm penalty

Random process variations are often composed of location dependent part and distance dependent correlated part. While an accurate extraction of process variation is a prerequisite of both process improvement and circuit performance prediction, it is not an easy task to characterize such complicated spatial random process from a limited number of silicon data. For this purpose, kriging model was introduced to silicon society. This work forms a modified kriging model with L1-norm penalty which offers improved robustness. With the help of Least Angle Regression (LAR) in solving a core optimization sub-problem, this model can be characterized efficiently. Some promising results are presented with numerical experiments where a 3X improvement in model accuracy is shown.