Proc. SPIE. 7638, Metrology, Inspection, and Process Control for Microlithography XXIV
KEYWORDS: Metrology, Optical lithography, Data modeling, Calibration, Inspection, Atomic force microscopy, Scanning electron microscopy, Process control, Optical proximity correction, Current controlled current source
Mask and metrology errors such as SEM (Scanning Electron Microscopy) measurement errors are currently not accounted for when calibrating OPC models. Nevertheless, they can lead to erroneous model parameters therefore causing inaccuracies in the model prediction if these errors are of the same order of magnitude than targeted modeling accuracy. In this study, we used a dedicated design of hundres of features exposed through a Focus Exposure Matrix for the metrology error, we compared the SEM measurements to AFM measurements for as much as 105 features exposed in various process conditions of does and defocus. These data have then been used in a OPC model calibration procedure. We show that the impact of the metrology error is not negligible and demonstrate the importance of taking into account these errors in order to improve the reliability of the OPC models.
Optical Proximity Correction (OPC) is used in lithography to increase the achievable resolution and pattern transfer
fidelity for IC manufacturing. Nowadays, immersion lithography scanners are reaching the limits of optical resolution
leading to more and more constraints on OPC models in terms of simulation reliability. The detection of outliers coming
from SEM measurements is key in OPC . Indeed, the model reliability is based in a large part on those measurements
accuracy and reliability as they belong to the set of data used to calibrate the model. Many approaches were developed
for outlier detection by studying the data and their residual errors, using linear or nonlinear regression and standard
deviation as a metric .
In this paper, we will present a statistical approach for detection of outlier measurements. This approach consists of
scanning Critical Dimension (CD) measurements by process conditions using a statistical method based on fuzzy CMean
clustering and the used of a covariant distance for checking aberrant values cluster by cluster. We propose to use
the Mahalanobis distance  in order to improve the discrimination of the outliers when quantifying the similarity within
each cluster of the data set.
This fuzzy classification method was applied on the SEM CD data collected for the Active layer of a 65 nm half pitch
technology. The measurements were acquired through a process window of 25 (dose, defocus) conditions. We were able
to detect automatically 15 potential outliers in a data distribution as large as 1500 different CD measurement. We will
discuss about these results as well as the advantages and drawbacks of this technique as automatic outliers detection for
large data distribution cleaning.
In advanced technology nodes, due to accuracy and computing time constraint, OPC has shifted from discrete simulation
to pixel based simulation. The simulation is grid based and then interpolation occurs between grid points. Even if the
sampling is done below Nyquist rate, interpolation can cause some variations for same polygon placed at different
location in the layout. Any variation is rounded during OPC treatment, because of discrete numbers used in OPC output
file. The end result is inconsistency in post-OPC layout, where the same input polygon will give different outputs,
depending on its position and orientation relative to the grid. This can have a major impact in CD control, in structures
like SRAM for example, where mismatching between gates can cause major issue.
There are some workarounds to minimize this effect, but most of them are post-treatment fix. In this paper, we will try to
identify and solve the root cause of the problem. We will study the relationship between the pixel size and the
consistency of post OPC results. The pixel size is often set based on optical parameters, but it might be possible to
optimize it around this value to avoid inconsistency. One can say that the optimization will highly depend on design and
not be possible for a real layout. As the range of pitch used in a design tends to decrease, thanks to fix pitch layouts, we
may optimize pixel size for a full layout.
At 45 and 32 nm nodes, one of the most critical layers is the Contact one. Due to the use of hyper NA imaging, the
depth of focus starts to be very limited.
Moreover the OPC is rapidly limited because of the increase of the pattern density. The limited surface in the dark field
region of a Contact layer mask enforces the edges movement to stop very quickly.
The use of SRAF (Sub Resolution Assist Feature) has been widely use for DOF enhancement of line and space layers
since many technology node. Recently, SRAF generated using inverse lithography have shown interesting DOF
improvement1. However, the advantage of the ideal mask generated by inverse lithography is lost when switching to a
manufacturable mask with Manhattan structures. For SRAF placed in rule based as well as Manhattan SRAF generated
after inverse lithography, it is important to know what their behavior is, in term of size and placement.
In this article we propose to study the placement of scatter-trenches assist features for the contact layer. For this we have
performed process window simulation with different SRAF sizes and distance to the main OPC. These results permit us
to establish the trends for size and placement of the SRAF.
Moreover we have also take a look of the advantages of using 8 surrounding SRAF (4 in vertical - horizontal and 4 at
45°) versus 4 surrounding SRAF. Based on these studies we have seen that there is no real gain of increasing the
complexity by adding additional SRAF.