Scatterometry Solutions for 14nm Half-Pitch BEOL Layers Patterned by EUV Single Exposure

Authored by: Sayantan Das, Joey Hung, Sandip Halder, Roy Koret, Igor Turovets, Anne- Laure Charley, Philippe Leray | SPIE 2021, February 1, 2021

To keep up with logic area scaling, BEOL dimensions have been reduced at an accelerated pace,
leading to ever smaller metal pitches and reduced cross-sectional areas of the wires. As a result, routing
congestion and a dramatic RC delay (resulting from an increased resistance-capacitance (RC) product)
have become important bottlenecks for further interconnect scaling, driving the need for introducing
new materials and integration schemes in the BEOL.
The current paper studies the damascene process flow that uses a single exposure EUV to create
metal lines and 2D patterns at metal half-pitch of 14nm, corresponding to the imec N5 node for logic
BEOL layer. A bright field mask with a negative tone resist process was used to develop trenches and
transfer these patterns into an oxide dielectric layer. Following this, the trenches were filled with
ruthenium (Ru) for electrically testing. Test vehicle included multiple structures, including E-test
resistance and capacitance structures, to allow a comprehensive study of the proposed process flow.
Metrology requirements and performance at various process steps will be discussed in this paper.
Our focus will be on the scatterometry methods that together with machine learning (ML) allow fast
and accurate measurements of multiple parameters of interest at large sampling. In the current paper,
we present results for inline measurements of line and space critical dimensions (CD), line edge
roughness (LER) – after patterning and after hard mask etch, and the prediction of the electrical
performance of the metal lines after Ru CMP. In addition, scatterometry ML capabilities for inline tipto-
tip (T2T) measurements are successfully demonstrated.
Keywords: EUV lithography, bright field EUV mask, pitch 28m, scatterometry, Ruthenium
damascene metallization, machine learning, process control, E-test prediction, resistance, and