Hybrid source mask optimization for robust immersion lithography

Abstract

To keep pace with the shrinkage of critical dimension, source and mask optimization (SMO) has emerged as a promising resolution enhancement technique to push the resolution of 193 nm argon fluoride immersion lithography systems. However, most current pixelated SMO approaches relied on scalar imaging models that are no longer accurate for immersion lithography systems with hyper-NA (NA>1). This paper develops a robust hybrid SMO (HSMO) algorithm based on a vector imaging model capable of effectively improving the robustness of immersion lithography systems to defocus and dose variations. The proposed HSMO algorithm includes two steps. First, the individual source optimization approach is carried out to rapidly reduce the cost function. Subsequently, the simultaneous SMO approach is applied to further improve the process robustness by exploiting the synergy in the joint optimization of source and mask patterns. The conjugate gradient method is used to update the source and mask pixels. In addition, a source regularization approach and source postprocessing are both used to improve the manufacturability of the optimized source patterns. Compared to the mask optimization method, the HSMO algorithm achieves larger process windows, i.e., extends the depth of focus and exposure latitude, thus more effectively improving the process robustness of 45 nm immersion lithography systems.

Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

“…1 Source mask optimization (SMO) improves the lithography performance by co-optimizing the illumination source and mask. 2 The optimized mask structure and source shape via SMO suffers from extreme complexity, especially for the pixelated mask and source, [3][4][5] which leads to difficulty in manufacturing. Furthermore, the parameters related to the process, such as the film stacks, postexposure bake (PEB), and photoresist development, have a strong impact on the process window (PW).…”

“…6,7 However, most published optimization technologies were implemented under fixed process conditions. 3,8,9 Moreover, lithography-tool parameters such as the numerical aperture (NA) and source parameter also determine the PW. 1 Actually, the parameters related to the mask, process, and lithography tool simultaneously impact the lithography performance.…”

“…At the same time, the complexity of the mask and source has been reduced compared with pixelated SMO in Ref. 3. In order to further improve the PW of the SMO work in Ref.…”

“…In order to further improve the PW of the SMO work in Ref. 3, we optimize the process parameters of the SMO. The DOF of the SMO with optimized process parameters is 13.54% larger than the SMO for the line-space pattern but still smaller than that of MPLCO.…”

Parametric source-mask-numerical aperture co-optimization for immersion lithography

“…Recently, we proposed a pixelated SMO based on a vector imaging model that significantly improved the simulation precision for lithography at the 45-nm node and beyond. 11,12 Previous SMO methods fixed the NA and fell short in considering the mutual impact of the NA with respect to the source and mask. Prior work has demonstrated that a larger NA could realize a higher resolution, but the DOF would decrease because of the relation DOF ¼ k 2 · λ∕ðNAÞ 2 , 13 where λ is the wavelength and k 2 is the process factor.…”