Dense mask registration fingerprint characterization to better understand and mitigate the metrology to device offset

Abstract

Over the past years, we have demonstrated that off-line mask registration measurements as measured by the Zeiss PROVE tool correlate very well (R2 > 0.96) with the on-wafer measurements. The first correlation study was based on scanner wafer alignment marks. Wafer alignment marks are metrology structures that can be readout by the alignment sensor inside the scanner. After we established the correlation, we continued the investigation by exploring overlay metrology targets. Also in this case, a very good correlation (R2 > 0.92) was found in the sub-nanometer regime. This could be achieved due the fact that overlay metrology targets are much smaller in size compared to the scanner wafer alignment marks. This enables the possibility to measure basically the same target areas by the PROVE and the ASML Yieldstar (YS:375) overlay metrology tool. The resulting residual level between mask and on-wafer measurements was less than 0.14-nm (99.7%) at wafer level (1×). The small mismatch that was remaining could be attributed to local mask writing variations inside the overlay metrology targets. The local variations triggered us to consider the mask writing impact on the placement errors for individual device features. Even for this case at device level, a very good correlation was observed between the mask registration measurements and the on-wafer results. This time, the on-wafer results were obtained by using a large field-of-view SEM. From all the findings above, we can basically conclude that off-line mask registration measurements can be used as overlay predictors in a production environment. This enables computational overlay metrology from scanner alignment marks, to overlay metrology targets, down to single device features. It might be obvious that these findings could be very helpful in predicting the mask overlay contribution as part of the intra-field overlay contribution without actually performing the onwafer measurements and consequently reducing the fab cycle time. At this point, we would like to zoom out and consider how all the acquired knowledge can be utilized in a production environment. Basically, the mask-related local placement errors of all features within the exposure field on a wafer can be predicted accurately based on off-line mask measurements. However, before improving the on-product overlay and hence the yield, more insight is required on how the mask writing fingerprint looks like. Do we see a global fingerprint or do local effects dominate the fingerprint? Is the fingerprint the same for the overlay metrology targets and device? Or do we observe a metrology to device (MTD) offset? In this paper, we will show a dense characterization of the mask registration fingerprint. Off-line measurements were done on overlay metrology targets as well as on a structure representing the device pattern. We will show how the overlay metrology sampling layout selection will impact the device overlay performance. Based on this understanding, we aim at providing a strategy and a path forward on how to mitigat...