We show that an overlay (OVL) metrology system based on a scanning electron microscope can achieve accurate registration of buried and resist (top) structures. The positions were determined by both Back Scattered Electrons (BSE) and Secondary Electrons (SE). The accuracy was quantified for After-Development Inspection (ADI) of an advanced EUVL process. Results by linear tracking showed accuracy below 0.4nm, robust across process variation and target designs. The influence of various measurement conditions, e.g. Field of View, on position and OVL tracking was negligible. The measurement methodology presented is applicable for both standalone High Voltage SEM (HV-SEM) registration targets and optical targets, such as the Advanced Imaging Metrology (AIM®) target used by Imaging Based Overlay (IBO) metrology systems. Using SEM ADI OVL results as a calibration for optical overlay metrology tools we can demonstrate significant improvements in the optical ADI OVL accuracy on small targets like AIM in-die (AIMid).
In this publication the authors have investigated both theoretically and experimentally the link between line edge roughness, target noise and overlay mark fidelity. Based on previous worki , a model is presented to explain how any given edge of a printed feature could have a mean position that varies stochastically (i.e., randomly, following a normal distribution) due to lithography stochastic variation. The amount of variation is a function of the magnitude of the LER (more accurately, all the statistical properties of the LER) and the length of the feature edge. These quantities have been analytically linked to provide an estimate for the minimum line length for both optical and e-beam based overlay metrology. The model results have been compared with experimental results from wafers manufactured at IMEC on both EUV and ArF lithographic processes developed for the 10 nm node, with extrapolation to the 5 nm node.
In this paper we discuss the mechanism by which process variations determine the overlay accuracy of optical metrology. We start by focusing on scatterometry, and showing that the underlying physics of this mechanism involves interference effects between cavity modes that travel between the upper and lower gratings in the scatterometry target. A direct result is the behavior of accuracy as a function of wavelength, and the existence of relatively well defined spectral regimes in which the overlay accuracy and process robustness degrades (`resonant regimes’). These resonances are separated by wavelength regions in which the overlay accuracy is better and independent of wavelength (we term these `flat regions’). The combination of flat and resonant regions forms a spectral signature which is unique to each overlay alignment and carries certain universal features with respect to different types of process variations. We term this signature the `landscape’, and discuss its universality. Next, we show how to characterize overlay performance with a finite set of metrics that are available on the fly, and that are derived from the angular behavior of the signal and the way it flags resonances. These metrics are used to guarantee the selection of accurate recipes and targets for the metrology tool, and for process control with the overlay tool. We end with comments on the similarity of imaging overlay to scatterometry overlay, and on the way that pupil overlay scatterometry and field overlay scatterometry differ from an accuracy perspective.
An infrared focal plane array (FPA) non-uniformity correction (NUC) method is considered, in which two-point calibration (TPC) table is calculated and stored at the factory, and the offset correction is carried out periodically during the mission. An InSb FPA is examined, and the stability of the NUC is measured over various periods (up to 9 months) and also under variations of the FPA temperature and integration time. We find that the NUC quality and its stability comply with the system requirements. We find, also, that the stability of the NUC is better maintained by using a single TPC table for the entire detector dynamic range, as compared to the multiple (piece-wise-linear) TPC. A simple phenomenological model of the detector non-linearity is proposed, which fits the experimental data better than the quadratic non-linearity model.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.