For the 28 nm node lithographic production steps, the process window for both overlay and CD are becoming
increasingly tight. The overlay stability of lithography tools must be at a level of 1-2 nm within the product cycle time,
while focus needs to be stable within 5 nm. Well-matched tools are crucial to improve the flexibility of tool usage and
the pressure for higher tool availability is allowing less time for periodic maintenance and tool recovery. Here, we
describe the way of working and results obtained with a long-term stability control application, containing a scanner
performance control system with a correction feedback loop deploying scatterometry. In this study the overlay
performance for immersion scanners was stabilized and the point-to-point difference to a reference is maintained at less
than 4 nm. The capability of tool recovery handling after interventions is demonstrated. Results of overlay matching
between machines are shown. The tool stability for focus was controlled in a range of less than 5 nm while improving the
total focus uniformity.
The fingerprint of the optical proximity effect, OPE, is required to develop each process node's optical proximity
correction (OPC) model. This model should work equally well on different exposure systems. However, small
differences in optical and mechanical properties in the lithographic system can lead to a different CD characteristic for a
given OPC. It becomes beneficial to match the OPE of one scanner to the scanner population in a fab. Here, we focus on
aspects of angle resolving scatterometry metrology used for OPE matching of two XT:1700i scanners and compare those
to SEM metrology. The capability of the scatterometry tool for monitoring the stability of OPE is evaluated.
Scatterometry allows measuring the side wall angle, SWA, of a resist profile and this can be used as a measure for focus.
Here, focus comparison by SWA is included into the matching process. For the application used here, the residual RMS
mismatch through pitch for scatterometry could be reduced to 0.2nm compared to 0.5nm for CD-SEM.
For the development of the most cost effective lithographic solutions for the 22nm node, the lithographic process and
relevant requirements on CDU and overlay need to be identified. In this work, 22nm logic SRAM is selected as use case
because FinFET SRAM cells are considered to be a potential successor to conventional planar transistors for 22nm node
chips. We focus on the back-end layers of FinFET SRAM, including metal and contact. Litho solutions simulated under
ideal scanner conditions with the ASML Brion TachyonTM SMO product are shown. This tool co-optimizes a pixilated
freeform source and a continuous transmission gray tone mask based on merit functions of edge placement error. Per
scenario, these simulations result in a set of preferred litho solutions with respective source and mask. These solutions
have to comply with an imaging metric characterized by MEEF and common PW based on typical fab requirements. In a
second step the previously generated solutions are evaluated for CDU analysis using realistic scanner error budget. The
purpose is to predict the CDU performance of scanner, process and reticle in order to identify the major contributors for
every scenario solution.
The fingerprint of optical proximity effect, OPE, is required to develop each process node's optical proximity correction
(OPC) model. The OPC model should work equally well on exposure systems of the type on which the model was
developed and of different type. Small differences in optical and mechanical scanner properties can lead to a different
CD characteristic for a given OPC model. It becomes beneficial to match the OPE of one scanner to the scanner
population in a fab. Here, we report on a matching technique based on measured features in resist employing either CDSEM
or scatterometry. We show that angle resolving scatterometry allows improving the metrology throughput and
repeatability. The sensitivity of the CD as a function of the scanner adjustments and the effect of scanner tuning can be
described more precisely by scatterometry using an identical number of printed features for measurement. In our
example the RMS deviation between the measured and the predicted tuning effect of scatterometry is 0.2 nm compared
to 0.8 nm of CD-SEM allowing to set tighter matching targets.