No abstract

In the domain of computational lithography, the performance of an optimized imaging solution is usually qualified with a full-chip posted-optical-proximity-correction lithography printing check to ensure the printing is defect free before committed for mask writing. It is thus highly preferable for the optimization process itself to be driven by the same defect detection mechanism towards a defect-free solution. On the other hand, the huge data size of chip layout poses great challenge to such optimization process, in terms of runtime and data storage. A gradient-based optimization scheme thus becomes necessary. To date, no successful engineering tool is capable of accommodating these two requirements at the same time. We demonstrate the technology of defect-driven gradient-based optimization to achieve a defect-free solution within practical runtime specification, using ASML’s computational lithography product Tachyon SMO.

Despite being crucial in an optical lithography process, “dose” has remained a relative concept in the computational lithography regime. It usually takes the form of a percentage deviation from a pre-identified “nominal condition” under the same illumination shape. Dose comparison between different illumination shapes has never been rigorously defined and modeled in numerical simulation to date. On the other hand, the exposure-limited nature of EUV lithography throughput demands the * illumination shape being optimized with the physical dose impact consciously taken into consideration. When the projection pupil is significantly obscured (as in the ASML EXE high NA scanner series), the lack of a proper physical dose constraint may lead to suboptimal energy utilization during exposure. In this paper, we demonstrate a method to accurately model the physical dose in an optical lithography process. The resultant dose concept remains meaningful in the context of a changing illumination pupil, which enables co-optimization of imaging quality and a throughput metric during the Source-Mask Optimization (SMO) phase, known as the Dose-Aware SMO. With a few realistic test cases we demonstrate the capability of Dose-Aware SMO in terms of improving EUV throughput via reducing the effective exposure time, in both regular and obscured projection systems. The physical dose modeling capability in computational lithography not only addresses those immediate challenges emergent from EUV throughput, but also opens the gate towards a broad class of exciting topics that are built upon physical dose, such as optical stochastic phenomena and so on.

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.