Informational Lithography Approach Based on Source and Mask Optimization

Abstract

Optical lithography is a critical technique to fabricate nano-scale semiconductor devices by replicating the layouts of integrated circuits from the lithography mask onto the silicon wafer. As the critical dimension of integrated circuits continuously shrinks, source and mask optimization (SMO) methods are extensively used to improve the resolution and image fidelity of lithography patterning. However, the theoretical lower bound of the lithography pattern error in the SMO framework is not yet understood. This paper introduces an informational lithography approach to unveil the information transmission mechanism in lithography systems under freeform illumination configurations. The lithography system is regarded as an information channel, where the mask pattern and the print image on the wafer are modeled as the statistical input and output signals, respectively. Subsequently, we derive the optimal information transfer (OIT) of the lithography system, which represents the best information transfer strategy rendering the least image distortion. Based on the OIT, we derive a lower bound for the lithography pattern error, and the corresponding optimal source pattern and optimal mask probability distribution. Finally, we propose a new SMO algorithm based on the information theoretical framework to effectively improve the lithography image fidelity compared to the existing gradient-based SMO algorithm.