Machine Learning for Predictive Electrical Performance Using OCD

Authored by: Sayantan Das, Joey Hung, Sandip Halder, Guillaume Schelcher, Roy Koret, Igor Turovets, Mohamed Saib, Anne-Laure Charley, Matthew Sandelbach, Avron Ger, Philippe Leray | SPIE 2019, February 1, 2019

With growing process complexity and the increasing number of process steps, early prediction of
device performance has become an important task in semiconductor manufacturing process control.
Machine learning (ML) techniques allow us to link in-line measurements to End-Of-Line (EOL)
electrical tests. In our paper, we use reflectance spectra obtained from the scatterometry tool to
predict both metal-line resistance and capacitance. We used IMEC N-14 process flow with LELE
EUV double patterning at the M1 stage. Special designs-of-experiments (DOE) for multiple
parameters allowed us to create a metrology solution for the entire process window and test its
accuracy for all POI. Induced variations of both line CDs and space CDs, together with specially
designed measurement sites, created wide variations both in metal-line resistance and capacitance.
Reflectance spectra were collected in-line at two process steps defining metal lines: HM etch and Cu
CMP at multiple targets, including E-test measurement sites, together with reference metrology for
overlay (OV) and CD (by using Diffraction-Based-Overlay (DBO) and CD SEM). EOL MT1
electrical test results were used for the ML training procedure for early prediction of patterning
effects (both CD and OL) on electrical performance enabling early decisions and cost reduction by
discarding out-of-spec wafers before they reached the electrical test. It was shown that ML OCD
predictive techniques are complimentary to the OCD model-based solutions for geometrical
parameters widely used for in-line APC.