Modeling Within-Die Spatial Correlation Effects for Process-Design Co-Optimization
ISQED '05 Proceedings of the 6th International Symposium on Quality of Electronic Design
Analysis and modeling of CD variation for statistical static timing
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Confidence scalable post-silicon statistical delay prediction under process variations
Proceedings of the 44th annual Design Automation Conference
A general framework for spatial correlation modeling in VLSI design
Proceedings of the 44th annual Design Automation Conference
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Adjustment-based modeling for statistical static timing analysis with high dimension of variability
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
IEEE Transactions on Information Theory
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
An efficient method for analyzing on-chip thermal reliability considering process variations
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Hi-index | 0.00 |
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.