At the core of Design-technology co-optimization (DTCO) processes, is the Design Space Exploration (DSE), where different design schemes and patterns are systematically analyzed and design rules and processes are co-optimized for optimal yield and performance before real products are designed. Synthetic layout generation offers a solution. With rules-based synthetic layout generation, engineers design rules to generate realistic layout they will later see in real product designs. This paper shows two approaches to generating full coverage of the design space and providing contextual layout. One approach relies on Monte Carlo methods and the other depends on combining systematic and random methods to core patterns and their contextual layout. Also, in this paper we present a hierarchical classification system that catalogs layouts based on pattern commonality. The hierarchical classification is based on a novel algorithm of creating a genealogical tree of all the patterns in the design space.
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.