We present an experimental study of pattern variability and defectivity, based on a large data set with more than 112 million SEM measurements from an HMI high-throughput e-beam tool. The test case is a 10nm node SRAM via array patterned with a DUV immersion LELE process, where we see a variation in mean size and litho sensitivities between different unique via patterns that leads to a seemingly qualitative differences in defectivity. The large available data volume enables further analysis to reliably distinguish global and local CDU variations, including a breakdown into local systematics and stochastics. A closer inspection of the tail end of the distributions and estimation of defect probabilities concludes that there is a common defect mechanism and defect threshold despite the observed differences of specific pattern characteristics. We expect that the analysis methodology can be applied for defect probability modeling as well as general process qualification in the future.
Multi-patterning is one of the commonly used processes to shrink device node dimensions. With the miniaturization of the device node and the increasing number of coated layers and lithography processes, needs for defect reduction and control are getting stronger. Although there are needs for detecting in-film defects during the lithography process, it is difficult to verify in-film defects detected by an optical inspection tool because in-film defects usually appear as SEM Non-Visuals (SNV) during defect review using a scanning electron microscope (SEM). This makes the tuning of optical inspection tools difficult since these defects may be considered as noise. However, if these defects are “real defects”, they will have a negative impact to manufacturing yield. In this paper, we investigate a new methodology to detect in-film defects with high sensitivity utilizing a broadband plasma inspection tool. This methodology is expected to allow the early detection of in-film defects before the pattern formation, hence improving device manufacturing yield.