Robust resolution enhancement optimization methods to process variations based on vector imaging model

摘要

Optical proximity correction (OPC) and phase shifting mask (PSM) are the most widely used resolution enhancementtechniques (RET) in the semiconductor industry. Recently, a set of OPC and PSM optimizationalgorithms have been developed to solve for the inverse lithography problem, which are only designed for thenominal imaging parameters without giving sufficient attention to the process variations due to the aberrations,defocus and dose variation. However, the effects of process variations existing in the practical optical lithographysystems become more pronounced as the critical dimension (CD) continuously shrinks. On the other hand, thelithography systems with larger NA (NA&0.6) are now extensively used, rendering the scalar imaging modelsinadequate to describe the vector nature of the electromagnetic field in the current optical lithography systems.In order to tackle the above problems, this paper focuses on developing robust gradient-based OPC and PSMoptimization algorithms to the process variations under a vector imaging model. To achieve this goal, an integrativeand analytic vector imaging model is applied to formulate the optimization problem, where the effectsof process variations are explicitly incorporated in the optimization framework. The steepest descent algorithmis used to optimize the mask iteratively. In order to improve the efficiency of the proposed algorithms, a set ofalgorithm acceleration techniques (AAT) are exploited during the optimization procedure.© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.