Identifying contributors to overlay variability using a model-less analysis method

Abstract

Overlay (OVL) has become a critical process control and metrology challenge for current and future process nodes of logic as well as memory devices. Especially with the advent of EUV lithography and the accompanying use of two lithographical techniques (EUV and 193nm immersion) for patterning of critical layers, there is an increased need for identifying variability and its root cause in the overlay signatures. Current variability analysis uses pre-defined models mostly related to describe variability and allocating them in standard categories. These models are usually tied to the applicable exposure capabilities. As the EUV to immersion layers undergo exposure with vastly different conditions, there is a need to analyze OVL without associating to specific models. In this paper, we report on a novel model-less method for analyzing overlay data containing complex intra-field signatures. The method can identify and quantify intra-field signatures variation within a wafer as well as across wafers. These signatures enable root cause analysis of contributors to overlay variability. We applied the method on data sets of long-term overlay data of an EUV to a 193-immersion layer. While, several applications of the method with respect to identifying exposure conditions are demonstrated specific to the EUV to immersion layer, it should be noted that the method is universally applicable to any imaging wavelength for current and reference layer.