Say good-bye to DOF: statistical process window analysis with inline lithographic process variations
Wenzhan Zhou, Minghao Tang, Huipeng Koh, Meisheng Zhou
Abstract
Wenzhan Zhou, Minghao Tang, Huipeng Koh, Meisheng Zhou
Abstract
In this paper we present one application of our new Advanced Lithography Parameters Extraction (ALPE) system in the lithography process window analysis. Compared with traditional DOF/EL based process window analysis or Monte Carlo approaches with pre-assumed process variations, our new approach uses real-life process variations (exposure, focus, and even PEB temperature, etc. if needed) collected by the new ALPE system. Different from pre-assumed process variations and independently measured process variations, all these process variations are directly correlated with inline CD variations, so we call them real-life process variations. Based on these real-life process variations, the estimation of final CD uniformity will be more accurate and objective. Comparing estimated CD uniformity of new process with CD uniformity of baseline process, it is possible for us to tell which process is better from statistical point of view.
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In this paper we present one application of our new Advanced Lithography Parameters Extraction (ALPE) system in the lithography process window analysis. Compared with traditional DOF/EL based process window analysis or Monte Carlo approaches with pre-assumed process variations, our new approach uses real-life process variations (exposure, focus, and even PEB temperature, etc. if needed) collected by the new ALPE system. Different from pre-assumed process variations and independently measured process variations, all these process variations are directly correlated with inline CD variations, so we call them real-life process variations. Based on these real-life process variations, the estimation of final CD uniformity will be more accurate and objective. Comparing estimated CD uniformity of new process with CD uniformity of baseline process, it is possible for us to tell which process is better from statistical point of view.
Key concepts: Process window, Process (computing), Computer science, Lithography, Process variation, Window (computing), Point process, Monte Carlo method