Due to the exponential growth of mobile wireless devices, low-power logic chips continue to drive device scaling. To enable sub-10 nm device scaling at an affordable cost, there is a strong need for single exposure advanced lithography. Extreme ultraviolet lithography (EUVL) is one of the most promising candidates to support the design rules for sub-10 nm. The aggressive mobile device design rules continue to push the critical dimension (CD) and pitch and put very stringent demands on the lithography performance such as pattern placement control, image contrast, critical dimension uniformity (CDU), and line width roughness (LWR). In this paper we report the latest advances in resolution enhancement techniques to address low k1 challenges in EUV lithography, specifically: minimizing the pattern placement error, enhancing the through-focus contrast, and reducing the impact of stochastic effects. We have developed an innovative source-mask optimization (SMO) method to significantly reduce edge placement errors (EPE)  . Aggressive design rules using the state-of-the-art NA of 0.33 of the NXE:3300B and its successor tools can have imaging below k1 = 0.4, which can extend the current process capabilities for single exposure high volume manufacturing (HVM). Burkhardt et al. reported in a previous study that inserting a sub-resolution assist feature (SRAF) within semi-isolated features introduces strong Bossung tilts and best focus shifts, and a general solution for random pitches is not apparent . Kang observed the same issues and proposed to introduce spherical aberrations to correct these effects while having a global impact on the full-chip . In this work we introduce a new methodology to apply SRAFs to improve contrast, reduce best focus shift, and improve process window. Finally, the lower number of photons of EUV and the small feature size brings serious issue of the stochastic effect that causes the line-edge-roughness (LER) and local CD uniformity (LCDU). Source power, photoresist, mask bias, and feature size all impact the stochastic effects that can result in large LER for low-k1 patterning. We incorporate an empirical LER model in the SMO NXE frame work to study how the pupil, mask, dose, and target CD can be optimized to reduce stochastic edge placement errors (SEPE). We believe that these advanced EUV RET techniques can support imaging k1 below 0.4 and extend single exposure for an NA of 0.33, as is used in the NXE:3300B and its successor tools.