In overlay (OVL) metrology the quality of measurements and the resulting reported values depend heavily on the measurement setup used. For example, in scatterometry OVL (SCOL) metrology a specific target may be measured with multiple illumination setups, including several apodization options, two possible laser polarizations, and multiple possible laser wavelengths. Not all possible setups are suitable for the metrology method as different setups can yield significantly different performance in terms of the accuracy and robustness of the reported OVL values. Finding an optimal measurement setup requires great flexibility in measurement, to allow for high-resolution landscape mapping (mapping the dependence of OVL, other metrics, and details of pupil images on measurement setup). This can then be followed by a method for analyzing the landscape and selecting an accurate and robust measurement setup. The selection of an optimal measurement setup is complicated by the sensitivity of metrology to variations in the fabrication process (process variations) such as variations in layer thickness or in the properties of target symmetry. The metrology landscape changes with process variations and maintaining optimal performance might require continuous adjustments of the measurement setup. Here we present a method for the selection and adjustment of an optimal measurement setup. First, the landscape is measured and analyzed to calculate theory-based accurate OVL values as well as quality metrics which depend on details of the pupil image. These OVL values and metrics are then used as an internal ruler (“self-reference”), effectively eliminating the need for an external reference such as CD-SEM. Finally, an optimal measurement setup is selected by choosing a setup which yields the same OVL values as the self-reference and is also robust to small changes in the landscape. We present measurements which show how a SCOL landscape changes within wafer, wafer to wafer, and lot to lot with intentionally designed process variations between. In this case the process variations cause large shifts in the SCOL landscape and it is not possible to find a common optimal measurement setup for all wafers. To deal with such process variations we adjust the measurement setup as needed. Initially an optimal setup is chosen based on the first wafer. For subsequent wafers the process stability is continuously monitored. Once large process variations are detected the landscape information is used for selecting a new measurement setup, thereby maintaining optimal accuracy and robustness. Methods described in this work are enabled by the ATL (Accurate Tunable Laser) scatterometry-based overlay metrology system.
In this publication the authors have investigated both theoretically and experimentally the link between line edge roughness, target noise and overlay mark fidelity. Based on previous worki , a model is presented to explain how any given edge of a printed feature could have a mean position that varies stochastically (i.e., randomly, following a normal distribution) due to lithography stochastic variation. The amount of variation is a function of the magnitude of the LER (more accurately, all the statistical properties of the LER) and the length of the feature edge. These quantities have been analytically linked to provide an estimate for the minimum line length for both optical and e-beam based overlay metrology. The model results have been compared with experimental results from wafers manufactured at IMEC on both EUV and ArF lithographic processes developed for the 10 nm node, with extrapolation to the 5 nm node.