Defects in nano-imprint lithography line patterns: Computational modelling and measurement accuracy

Abstract

NIL patterns frequently suffer from the presence of defects such as missing lines or dots which degrade their properties and functionality. Due to their low density and nanosize, the measurement of their fraction is challenging nanometrology trade-off between resolution and measurement range. In this paper, we focus on the use of range-limited SEM images and explore the benefits of a computational modeling approach to simulate the measurement process and estimate the statistics and accuracy of the measurement of missing lines in patterns. The main questions we address have to do with the choice of the parameters available in the measurement process such as the number of acquired images, their magnification defining the lines included in images and the position (overlapped or not) at line pattern. The missing lines can have both uncorrelated and correlated positions in pattern. In the case of positive correlations, the defects are aggregated whereas in the opposite case of negative correlations they are arranged in periodic-like positions. We found that for uncorrelated defects, the critical parameter is the total number of lines included in the measurement process while the image position do not have any impact on the measurement accuracy. On the contrary, when correlations in defect positions are considered, the number of images and the number of lines per image differentiate their effects on the accuracy of the result while the arrangement of images along pattern also plays a crucial role in the measurement process.