PROCEEDINGS ARTICLE | March 13, 2018
Proc. SPIE. 10585, Metrology, Inspection, and Process Control for Microlithography XXXII
KEYWORDS: Metrology, Etching, Silicon, Scanning electron microscopy, Image filtering, Line width roughness, Line edge roughness, Photoresist processing, Standards development
Edge placement error (EPE) has become an increasingly critical metric to enable Moore’s Law scaling. Stochastic variations, as characterized for lines by line width roughness (LWR) and line edge roughness (LER), are dominant factors in EPE and known to increase with the introduction of EUV lithography. However, despite recommendations from ITRS, NIST, and SEMI standards, the industry has not agreed upon a methodology to quantify these properties. Thus, differing methodologies applied to the same image often result in different roughness measurements and conclusions. To standardize LWR and LER measurements, Fractilia has developed an unbiased measurement that uses a raw unfiltered line scan to subtract out image noise and distortions. By using Fractilia’s inverse linescan model (FILM) to guide development, we will highlight the key influences of roughness metrology on plasma-based resist smoothing processes. Test wafers were deposited to represent a 5 nm node EUV logic stack. The patterning stack consists of a core Si target layer with spin-on carbon (SOC) as the hardmask and spin-on glass (SOG) as the cap. Next, these wafers were exposed through an ASML NXE 3350B EUV scanner with an advanced chemically amplified resist (CAR). Afterwards, these wafers were etched through a variety of plasma-based resist smoothing techniques using a Lam Kiyo conductor etch system. Dense line and space patterns on the etched samples were imaged through advanced Hitachi CDSEMs and the LER and LWR were measured through both Fractilia and an industry standard roughness measurement software. By employing Fractilia to guide plasma-based etch development, we demonstrate that Fractilia produces accurate roughness measurements on resist in contrast to an industry standard measurement software. These results highlight the importance of subtracting out SEM image noise to obtain quicker developmental cycle times and lower target layer roughness.
