Process and reliability risks have become critically important during mass production at advanced technology nodes even with Extreme Ultraviolet Lithography (EUV) illumination. In this work, we propose a design-for-manufacturability solution using a set of new rules to detect high risk design layout patterns. The proposed methods improve design margins while avoiding area overhead and complex design restrictions. In addition, the proposed method introduces an in-design pattern replacement with automatically generated fixing hints to improve all matched locations with identified patterns.
Continuous scaling of CMOS process technology to 7nm (and below) has introduced new constraints and challenges in determining Design-for-Yield (DFY) solutions. In this work, traditional solutions such as improvements in redundancy and in compensating target designs for low process window margins are extended to meet the additional constraints of complex 7nm design rules. Experiments conducted on 7nm industrial designs demonstrate that the proposed solution achieves 9.1%-41% redundant-via-rate improvements while ensuring all 7nm design rule constraints are met.
As the typical litho hotspot detection runtime continue to increase with sub-10nm technology node due to increasing design and process complexity, many DFM techniques are exploring new methods that can expedite some of their advanced verification processes. The benefit of improved runtimes through simulation can be obtained by reducing the amount of data being sent to simulation. By inserting a pattern matching operation, a system can be designed such that it only simulates in the vicinity of topologies that somewhat resemble hotspots while ignoring all other data. Pattern Matching improved overall runtime significantly. However, pattern matching techniques require a library of accumulated known litho hotspots in allowed accuracy rate. In this paper, we present a fast and accurate litho hotspot detection methodology using specialized machine learning. We built a deep neural network with training from real hotspot candidates. Experimental results demonstrate Machine Learning’s ability to predict hotspots and achieve greater than 90% detection accuracy and coverage, with best achieved accuracy 99.9% while reducing overall runtime compared to full litho simulation.