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.
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