No abstract

With the adoption of extreme ultraviolet (EUV) lithography for high-volume production of advanced nodes, stochastic variability and resulting failures, both post litho and post etch, have drawn increasing attention. There is a strong need for accurate models for stochastic edge placement error (SEPE) with a direct link to the induced stochastic failure probability (FP). Additionally, to prevent stochastic failure from occurring on wafers, a holistic stochastic-aware computational lithography suite of products is needed, such as stochastic-aware mask source optimization (SMO), stochastic-aware optical proximity correction (OPC), stochastic-aware lithography manufacturability check (LMC), and stochastic-aware process optimization and characterization. In this paper, we will present a framework to model both SEPE and FP. This approach allows us to study the correlation between SEPE and FP systematically and paves the way to directly correlate SEPE and FP. Additionally, this paper will demonstrate that such a stochastic model can be used to optimize source and mask to significantly reduce SEPE, minimize FP, and improve stochastic-aware process window. The paper will also propose a flow to integrate the stochastic model in OPC to enhance the stochastic-aware process window and EUV manufacturability.