High Performance Lithography Hotspot Detection with Hierarchically Refined Machine Learning Methods

Lithography hotspot detection faces three challenges: 1) real hotspots are now harder to fix; 2) false alarm rate must be minimized; 3), full chip physical verification and optimization require fast turnaround. We propose a lithographic hotspot detection flow with ultra-fast speed and high fidelity that is especially suitable for guiding lithography-friendly design under real manufacturing conditions.

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

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

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

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

[6]  Philippe Hurat,et al.  Automated full-chip hotspot detection and removal flow for interconnect layers of cell-based designs , 2007, SPIE Advanced Lithography.

[7]  H. Yao,et al.  Efficient Process-Hotspot Detection Using Range Pattern Matching , 2006, 2006 IEEE/ACM International Conference on Computer Aided Design.

[8]  David Z. Pan,et al.  RADAR: RET-aware detailed routing using fast lithography simulations , 2005, Proceedings. 42nd Design Automation Conference, 2005..

[9]  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.

[10]  J. Andres Torres,et al.  Directional 2D functions as models for fast layout pattern transfer verification , 2009, Advanced Lithography.

[11]  Yao-Wen Chang,et al.  Predictive Formulae for OPC With Applications to Lithography-Friendly Routing , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[12]  Costas J. Spanos,et al.  Automatic hotspot classification using pattern-based clustering , 2008, SPIE Advanced Lithography.