Proceedings Article | 5 October 2023
KEYWORDS: Semiconducting wafers, Distortion, Scanning electron microscopy, Metrology, Image processing, Contour extraction, Optical lithography, Lithography, Data modeling, Critical dimension metrology
Background: Stochastic effects are manifested by local variabilities in critical dimension (CD), shape, size and placement of patterns. On previous works, a commercial software has been used to extract contours from CDSEM images, from which various metrics are computed to quantify these variabilities. Last year, an image processing system has been implemented to monitor these indicators in a high-volume manufacturing (HVM) environment. This system automatically extracts these contour-based metrics from the in-line images. Aim: The study has been divided into two parts. The first objective was to show the ability to detect a mismatch between two lithography clusters, not revealed by traditional CD in-line trends. Since investigations identified that this mismatch was caused by differences in the rinse recipes, the second objective was to gain a deeper understanding of the impact of rinse on both metrology and patterning. Approach: In addition to the traditional CD measurements, the following metrics are also monitored: Local CD Uniformity (LCDU), Pattern CD Uniformity (PCDU) and Centroid Shift Uniformity (CSU). Results: Baselines for the 28 nm contact process have been established, providing new indicators to compare and monitor processes and tools in the fab. PCDU computed on post-lithography images proved to be an effective detector of clusters mismatching, not identified by the CD criterion. Upon investigation, engineers discovered differences in the rinse recipes between the two lithography tools. To analyze the influence of rinse, an R&D experiment was conducted, repeating this very same step 2, 4 and 6 times in a row. As rinse steps number increased, two observations have been made: SEM images are more distorted, and the shape variability indicator (PCDU) increases. The most likely explanation is that increased friction with the deionized water during the rinse results in more stored electrical charges at the wafer surface, which affects the resist (increased “roughness”) and disturbs the SEM image acquisition. Conclusions: By implementing a remote metrology system to perform extensive analysis of in-line CD-SEM images with a contour extraction software, a cluster mismatching (not from the CD point of view) was identified in a HVM environment. This enhanced in-line monitoring enabled us to find the parameter to be adjusted to match them, thereby avoiding any adverse effects. Experiments confirmed that rinse negatively affects both SEM image fidelity (increased distortion) and process uniformity (decreased shape uniformity).
