A novel fuzzy matching model for lithography hotspot detection

In advanced IC manufacturing, as the gap between lithography optical wavelength and feature size increases, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which can dynamically tune appropriate fuzzy regions around known hotspots. Based on this model, we develop a fast algorithm for lithography hotspot detection with very low chances of false-alarm. Our results are very encouraging with under 0.56 CPU-hrs/mm2 runtime.

[1]  Juhwan Kim,et al.  Hotspot detection on post-OPC layout using full-chip simulation-based verification tool: a case study with aerial image simulation , 2003, SPIE Photomask Technology.

[2]  Philippe Hurat,et al.  Layout printability optimization using a silicon simulation methodology , 2004, International Symposium on Signals, Circuits and Systems. Proceedings, SCS 2003. (Cat. No.03EX720).

[3]  Andrew B. Kahng,et al.  Fast dual graph-based hotspot detection , 2006, SPIE Photomask Technology.

[4]  Jingyu Xu,et al.  Accurate detection for process-hotspots with vias and incomplete specification , 2007, 2007 IEEE/ACM International Conference on Computer-Aided Design.

[5]  Jie Yang,et al.  DRC Plus: augmenting standard DRC with pattern matching on 2D geometries , 2007, SPIE Advanced Lithography.

[6]  Kazuhiko Takahashi,et al.  Study of hot spot detection using neural networks judgment , 2007, Photomask Japan.

[7]  Yici Cai,et al.  Efficient range pattern matching algorithm for process-hotspot detection , 2008, IET Circuits Devices Syst..

[8]  David Z. Pan,et al.  Machine learning based lithographic hotspot detection with critical-feature extraction and classification , 2009, 2009 IEEE International Conference on IC Design and Technology.

[9]  Frank Liu,et al.  Predicting variability in nanoscale lithography processes , 2009, 2009 46th ACM/IEEE Design Automation Conference.

[10]  Malgorzata Marek-Sadowska,et al.  Detecting context sensitive hot spots in standard cell libraries , 2009, Advanced Lithography.

[11]  Costas J. Spanos,et al.  Clustering and pattern matching for an automatic hotspot classification and detection system , 2009, Advanced Lithography.

[12]  Malgorzata Marek-Sadowska,et al.  Rapid layout pattern classification , 2011, 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011).

[13]  J. Andres Torres,et al.  Multi-selection method for physical design verification applications , 2011, Advanced Lithography.

[14]  Malgorzata Marek-Sadowska,et al.  Efficient approach to early detection of lithographic hotspots using machine learning systems and pattern matching , 2011, Advanced Lithography.

[15]  Fedor G. Pikus,et al.  High performance lithographic hotspot detection using hierarchically refined machine learning , 2011, 16th Asia and South Pacific Design Automation Conference (ASP-DAC 2011).

[16]  David Z. Pan,et al.  EPIC: Efficient prediction of IC manufacturing hotspots with a unified meta-classification formulation , 2012, 17th Asia and South Pacific Design Automation Conference.

[17]  J. Andres Torres,et al.  ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite , 2012, 2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).