Optical overlay metrology has been used for years as the baseline for overlay control, measuring an optical target in the scribe line with optimized design to best match the on-product overlay. However, matching the optical target overlay measurements to the real on-product overlay becomes a serious challenge for most advanced technology nodes and forces the industry to develop different or complementary solutions. To identify and better quantify the different, well-known overlay accuracy detractors, in this work we have used optical and state-of-the-art electron beam technologies (eBeam) to measure on-product and on-optical target overlay errors of a wafer processed at imec using 5 nm technology node design rules and intentionally introduced overlay skews of +10 and -10 nm in x and y axis. The overlay errors as measured by the SEM eBeam system, equipped with elluminator™ technology which enables fast see through measurements of overlay which has been compared with (X-sectional) STEM-HAADF reference overlay metrology data. The on-product and optical target SEM overlay measurements show very similar wafer maps, in line with the applied overlay errors during the lithography exposure step. eBeam and TEM data show excellent correlation for the on-product overlay errors and the eBeam data also reveal a significant bias of ~ 6 nm between on-product and on-target overlay errors. From these results it can be concluded that manufacturing of advanced devices which require accurate OPO control, will need new metrology strategies that combine eBeam and optical or, eventually, use only eBeam technologies to guarantee effective overlay control with sufficient accuracy.
Each of six scanning transmission electron microscope (STEM) system alignments were characterized with a sensitivity test to better understand the impact of the alignment on microscope automation for acquisition and metrology. The upper and lower limits of the alignment sensitivity combined with the accuracy of the automated system alignments are used to determine the process capability index (Cpk). The primary limiting factor for the alignment sensitivity was passing the metrology dynamic precision criteria of <0.3nm. Strong alignment offset showed impact on the autofunction robustness, but was marginal in comparison to the metrology sensitivity. All six of the STEM system alignments resulted in Cpk values greater than 1.3, supporting a three sigma quality process. Based on these results, criteria can be defined for alignment offset limits that will trigger automated preventative maintenance (APM) re-alignments and ensure that automated STEM metrology will meet the accuracy and precision requirements for adequate process control.
Metrology of 3D NAND device architecture is challenging due to structural complexity, low signal to noise and contrast to noise ratio in the electron micrographs. Efficient, automated tools that can measure critical dimensions of 3D NAND in electron micrographs can be a part of solution for process monitoring, uniformity control and structural modelling through OCD, CD-SEM, e-beam tech., etc. In this paper we present an automated technique based on a snake algorithm in a multi-stage, scale-space framework to delineate continuous interfaces between different materials/layers of 3D NAND cells. The snake algorithm takes an initial contour and forces the initial contours to move and deform towards the interface between different material layers using an iterative energy minimization process while preserving the continuity and smoothness of the contour. At the end of energy minimization, the interface between different materials such as central hole, core silicon oxide, poly silicon, tunneling silicon oxide, silicon nitride with storage function, blocking silicon oxide, outer metal layers, etc., are delineated and marked by labelled contours. Prior knowledge, if available, about the number of material-layers and approximate distance between them can be used to improve the efficiency of the process. The proposed method transforms the micrograph into a digital metrology image where critical dimensions such as thickness of the materials, shape of the material layers, etc., can be automatically measured. The additional information provided by continuous contours can be used with ‘Big Data’ analytics to uncover patterns, variations, and outliers that may go unnoticed in discrete measurement data.