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Proceedings Paper

Machine learning for predictive electrical performance using OCD
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Paper Abstract

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 N14 process flow with LELE 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 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.

Paper Details

Date Published: 26 March 2019
PDF: 9 pages
Proc. SPIE 10959, Metrology, Inspection, and Process Control for Microlithography XXXIII, 109590F (26 March 2019); doi: 10.1117/12.2515806
Show Author Affiliations
Sayantan Das, IMEC (Belgium)
Joey Hung, Nova Measuring Instruments Ltd. (Israel)
Sandip Halder, IMEC (Belgium)
Guillaume Schelcher, IMEC (Belgium)
Roy Koret, Nova Measuring Instruments Ltd. (Israel)
Igor Turovets, Nova Measuring Instruments Ltd. (Israel)
Mohamed Saib, IMEC (Belgium)
Anne-Laure Charley, IMEC (Belgium)
Matthew Sendelbach, Nova Measuring Instruments Inc. (United States)
Avron Ger, Nova Measuring Instruments Ltd. (Israel)
Philippe Leray, IMEC (Belgium)


Published in SPIE Proceedings Vol. 10959:
Metrology, Inspection, and Process Control for Microlithography XXXIII
Vladimir A. Ukraintsev; Ofer Adan, Editor(s)

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