Gradient-based resolution enhancement optimization methods based on vector imaging model

摘要

Recently, a set of gradient-based optical proximity correction (OPC) and phase shifting mask (PSM) optimizationmethods have been developed to solve for the inverse lithography problem under scalar imaging models, which areonly accurate for numerical apertures (NA) less than approximately 0.4. However, as the lithography technologynode enters the 45nm realm, immersion lithography systems with hyper-NA (NA&1) are now extensively usedin the semiconductor industry. For the hyper-NA lithography systems, the vector nature of the electromagneticfield must be taken into account, leading to the vector imaging models. Thus, the OPC and PSM optimizationapproaches developed under the scalar imaging models are inadequate to enhance the resolution in the immersionlithography systems. This paper focuses on developing gradient-based OPC and PSM optimization algorithmsunder vector imaging models. The mask optimization framework is first formulated, in which the imagingprocess of the optical lithography system is represented by an integrative and analytic vector imaging model.The steepest descent algorithm is then used to optimize the mask iteratively. Subsequently, a generalized waveletpenalty (GWP) is proposed to improve the manufacturability of the mask, and results in smaller pattern errorsand CD errors than the traditional wavelet penalty (WP). Finally, a set of algorithm acceleration techniques areexploited to speed up the proposed algorithms.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.