Automated mask and wafer defect classification using a novel method for generalized CD variation measurements

V. Verechagin; R. Kris; I. Schwarzband; A. Milstein; B. Cohen; A. Shkalim; S. Levy; D. Price; E. Bal

doi:10.1117/12.2302714

13 March 2018 Automated mask and wafer defect classification using a novel method for generalized CD variation measurements

V. Verechagin, R. Kris, I. Schwarzband, A. Milstein, B. Cohen, A. Shkalim, S. Levy, D. Price, E. Bal

Author Affiliations +

Proceedings Volume 10585, Metrology, Inspection, and Process Control for Microlithography XXXII; 1058531 (2018) https://doi.org/10.1117/12.2302714
Event: SPIE Advanced Lithography, 2018, San Jose, California, United States

Abstract

Over the years, mask and wafers defects dispositioning has become an increasingly challenging and time consuming task. With design rules getting smaller, OPC getting complex and scanner illumination taking on free-form shapes - the probability of a user to perform accurate and repeatable classification of defects detected by mask inspection tools into pass/fail bins is reducing. The critical challenging of mask defect metrology for small nodes ( < 30 nm) was reviewed in [1]. While Critical Dimension (CD) variation measurement is still the method of choice for determining a mask defect future impact on wafer, the high complexity of OPCs combined with high variability in pattern shapes poses a challenge for any automated CD variation measurement method. In this study, a novel approach for measurement generalization is presented. CD variation assessment performance is evaluated on multiple different complex shape patterns, and is benchmarked against an existing qualified measurement methodology.

Citation Download Citation

V. Verechagin, R. Kris, I. Schwarzband, A. Milstein, B. Cohen, A. Shkalim, S. Levy, D. Price, and E. Bal "Automated mask and wafer defect classification using a novel method for generalized CD variation measurements", Proc. SPIE 10585, Metrology, Inspection, and Process Control for Microlithography XXXII, 1058531 (13 March 2018); https://doi.org/10.1117/12.2302714

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