Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature Extraction PROJECT TITLE :Machine-Learning-Based Hotspot Detection Using Topological Classification and Critical Feature ExtractionABSTRACT:Because of the widening sub-wavelength lithography gap in advanced fabrication technology, lithography hotspot detection has become an essential task in design for manufacturability. Unlike current state-of-the-art works, which unite pattern matching and machine-learning engines, we fully exploit the strengths of Machine Learning using novel techniques. By combing topological classification and critical feature extraction, our hotspot detection framework achieves very high accuracy. Furthermore, to speed-up the evaluation, we verify only possible layout clips instead of full-layout scanning. We utilize feedback learning and present redundant clip removal to reduce the false alarm. Experimental results show that the proposed framework is very accurate and demonstrates a rapid training convergence. Moreover, our framework outperforms the 2012 CAD contest at International Conference on Computer-Aided Design (ICCAD) winner on accuracy and false alarm. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Maximum Convex Subgraphs Under I/O Constraint for Automatic Identification of Custom Instructions Accurate Predictions of Process-Execution Time and Process Status Based on Support-Vector Regression for Enterprise Information Systems