As the litho hotspot detection runtime is currently in a continuous increase with sub-10nm technology nodes due to the increase of the design and process complexity, new methods and approaches are needed to improve the runtime while guaranteeing high accuracy rate. Machine-Learning Fast LFD (ML-FLFD) is a new flow that uses a specialized machine learning technique to provide fast and accurate litho hotspot detection. This methodology is based on having input data to train the machine learning model during the model preparation phase. Current ML-FLFD techniques depend on collecting hotspots (HS) and Non hotspots (NHS) data from the drawn layer in order to train the model. In this paper, we present a new technique where we use the retarget data to train the machine learning model instead of using the drawn hotspot data. Using retargeting data is getting one step closer to the actual printed contours which gives a better insight about the hotspots of the manufactured wires during the machine learning model training step. The effect of using closer data to the printed contours will be reflected on both the accuracy and the extra rate which will reduce simulation area. In the different sections of this paper, we will compare the new approach of using retarget data as a ML input to the current technique of using drawn data. Pros and cons of the two approaches will be listed in details including the experimental results of hotspot accuracy and litho simulation area.
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.
Sub-20nm node designs are getting more sophisticated, and printability issues become more critical which need more advanced techniques to fix. It is mandatory for designers to run lithography checks before tapeout, and it is very challenging to fix all of the generated hotspots manually without introducing unintentional hotspots, or DPT violations. This paper presents a methodology for fixing hotspots on DPT layouts, using the same Model Based Hints (MBH) engine used for detecting hotspots. The fix is based on DRC and DPT constrained minimum movement of edges causing the hotspot, which guarantees that the fix does not violate any of the specified DRC or DPT constraints, nor does it need recoloring. The fix is extended along multilayers to fulfill the specified DRC and DPT constraints and guarantees circuit connectivity along the layers stack. This multilayers approach fixes hotspots that were impossible to fix previously. This methodology is demonstrated on industrial designs, where real hotspots were fixed and the fixing rate is reported.