As device size continues to shrink, stochastic-induced roughness of resist features exposed by photolithography is of increasing concern to the semiconductor industry. In this paper, we propose an end-to-end approach for line roughness analysis by using the Line Roughness Module from our CDU solution family, which is a part of HMI’s metrology SEM tool the eP5. Simulated Scanning Electron Microscope (SEM) images of line/space patterns are used to verify the ability of the Module to reliably extract roughness related metrics. A set of imec EUV ADI images collected on our metrology SEM tool are analyzed by the Line Roughness Module, and wafer signature maps of various roughness metrics are obtained. These wafer maps not only help to analyze different roughness contribution sources, but also provide insights about feature roughness in a systematic way. Such information can be further used in a feedback loop to the scanner for model correction and process control.
In semiconductor industry, as physical sizes of integrated circuit (IC) components continue to shrink, critical dimension (CD) metrology plays an important role in manufacturing process monitor and control. However, when prior knowledge of E-beam tool conditions and statistics of underlying imaging samples are limited or missing, metrology parameters (such as imaging conditions and CD measurement parameters) are often selected empirically and not optimized in terms of measurement accuracy or precision. Common practice involved in fine-tuning some of the parameters may result in a time-consuming trial-and-error cycle.
In this paper, we propose a guidance system to provide an optimized set of metrology parameters given a line/space pattern image or images of scanning electron microscope (SEM). The proposed system models input condition with a comprehensive set of model parameters and then statistical analysis is done based on modeling outputs. A set of metrology guidelines, such as measurement parameters and achievable precisions, can be recommended by the proposed system. The validity of our method has been demonstrated by comparing the recommended parameters with the optimal parameters found by brute-force search on a set of 100 SEM images of line/space patterns.
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