Evaluation of high NA thin resist metrology and stochastic performance

Abstract

The next milestone in the resolution of high-volume manufacturing will be achieved by High-NA EUV Lithography (HiNA EUVL) systems. In addition to the development of high-NA EUV scanners, the implementation of next generation resist materials remains a critical challenge. Thinner resists are required to accommodate DoF decrease on higher NA physics, which may lead to e-beam metrology and e-beam inspection sensitivity loss due to less electron signal and more severe stochastic failures and variabilities. Due to worse resist imaging contrast and larger pattern roughness in HiNA EUVL, accurate and high resolution CD and defect metrology as well as precise process window (PW) evaluation which considers stochastic failure rate (FR) are needed for resist screening to support the gradual reduction of resist thickness.In this paper we present a thin resist evaluation flow based on next generation e-beam metrology tool (eP6), including metrology setup and qualification and stochastic-aware process window (SAPW) analysis. We will show ADI (after development imaging) evaluation results for a contact hole array with pitch of 32nm for three CAR resist deposited thicknesses from 60nm to 30nm (note: ~40% resist thickness loss post-development). First, we will illustrate some of the metrology responses associated with thin resist and highlight the benefits of improved precision (0.7x), resolution (1.5nm) and sensitivity of next generation metrology tool. For each resist thickness, we will show CD, LCDU and defect measurements across a focus-exposure matrix. LCDU decomposition will be used to quantify SEM, mask, and stochastic contributions to the total measured LCDU. Finally, we will exhibit the SAPW result (Dose-to-Size, Depth of Focus, Exposure Latitude … etc) for the range of resist thicknesses, which can be used to optimize process centering and qualify thin resist performance for HiNA with high correlation coefficient (> 0.9) between measured FR and modeled FR.