KEYWORDS: Photomasks, Data modeling, Calibration, 3D modeling, Semiconducting wafers, Scanning electron microscopy, Optical proximity correction, Photoresist processing, Process modeling, Silicon
To reduce cost, implant levels usually use masks fabricated with older generation mask tools, such
as laser writers, which are known to introduce significant mask errors. In fact, for the same implant
photolithography process, Optical Proximity Correction (OPC) models have to be developed
separately for the negative and positive mask tones to account for the resulting differences from the
mask making process. However, in order to calibrate a physical resist model, it is ideal to use single
resist model to predict the resist performance under the two mask polarities. In this study, we show
our attempt to de-convolute mask error from the Correct Positive (CP) and Correct Negative (CN)
tone CD data collected from bare Si wafer and derive a single resist model. Moreover, we also
present the predictability of this resist model over a patterned substrate by comparing simulated
CD/profiles against wafer data of various features.
KEYWORDS: 3D modeling, Critical dimension metrology, Data modeling, Process modeling, Scanning electron microscopy, Semiconducting wafers, Etching, Optical lithography, Printing, 3D image processing
In this paper we will demonstrate how a 3D physical patterning model can act as a forensic tool for OPC
and ground-rule development. We discuss examples where the 2D modeling shows no issues in printing
gate lines but 3D modeling shows severe resist loss in the middle. In absence of corrective measure, there is
a high likelihood of line discontinuity post etch. Such early insight into process limitations of prospective
ground rules can be invaluable for early technology development. We will also demonstrate how the root
cause of broken poly-line after etch could be traced to resist necking in the region of STI step with the help
of 3D models. We discuss different cases of metal and contact layouts where 3D modeling gives an early
insight in to technology limitations. In addition such a 3D physical model could be used for early resist evaluation and selection for required ground-rule challenges, which can substantially reduce the cycle time for process development.
In this paper, we report large scale three-dimensional photoresist model calibration and validation
results for critical layer models that span 32 nm, 28 nm and 22 nm technology nodes. Although
methods for calibrating physical photoresist models have been reported previously, we are unaware
of any that leverage data sets typically used for building empirical mask shape correction models. .
A method to calibrate and verify physical resist models that uses contour model calibration data sets
in conjuction with scanning electron microscope profiles and atomic force microscope profiles is
discussed. In addition, we explore ways in which three-dimensional physical resist models can be
used to complement and extend pattern hot-spot detection in a mask shape validation flow.
Optical and Process Correction in the 45nm node is requiring an ever higher level of characterization. The greater
complexity drives a need for automation of the metrology process allowing more efficient, accurate and effective use of
the engineering resources and metrology tool time in the fab, helping to satisfy what seems an insatiable appetite for data
by lithographers and modelers charged with development of 45nm and 32nm processes. The scope of the work
referenced here is a 45nm design cycle "full-loop automation", starting with gds formatted target design layout and
ending with the necessary feedback of one and two dimensional printed wafer metrology.
In this paper the authors consider the key elements of software, algorithmic framework and Critical Dimension Scanning
Electron Microscope (CDSEM) functionality necessary to automate its recipe creation. We evaluate specific problems with the methodology of the former art, "on-tool on-wafer" recipe construction, and discuss how the implementation of the design based recipe generation improves upon the overall metrology process. Individual target-by-target construction, use of a one pattern recognition template fits all approach, a blind navigation to the desired measurement
feature, lengthy sessions on tool to construct recipes and limited ability to determine measurement quality in the resultant
data set are each discussed as to how the state of the art Design Based Metrology (DBM) approach is implemented.
The offline created recipes have shown pattern recognition success rates of up to 100% and measurement success rates of
up to 93% for line/space as well as for 2D Minimum/Maximum measurements without manual assists during measurement.
Performing model based optical proximity correction (MB-OPC) is an essential step in the production of advanced integrated circuits manufactured with optical lithography technology. The accuracy of these models highly depends on the experimental data used in the model development and on the appropriate selection of the model parameters. The optical and resist model parameters selected during model build have a significant impact on the OPC model accuracy, run time, and stability. In order to avoid excessively high run times as well as ensure acceptable results, a compromise must be made between OPC run time and model accuracy. The modeling engineer has to optimize the necessary model parameters in order to find a good trade-off that achieves acceptable accuracy with reasonable run time. In this paper, we investigate the effect of some selected optical and resist model parameters on the OPC model accuracy, run time, and stability.
Optimal Proximity Correction (OPC) models are calibrated with Scanning Electron Microscope (SEM) data where the measurement uncertainty vary among pattern types (i.e., line versus space, 1D versus 2D and small versus large). The quality of the SEM measurement uncertainty's impact on OPC model integrity is mitigated through a weighting scheme. Statistical methods such as relating the weight to the SEM measurements standard deviation require more measurements per calibration structure than economically feasible. Similarly, the use of experience and engineering judgment requires many iterations before some reasonable weighting scale is determined. In this paper we present the results of OPC model fitness statistics associated with metrology based weights (MtBW) versus model based weights (MBW). The motivation for the latter approach is the promise for an unbiased, consistent, and efficient estimate of the model parameters.
Performing model based optical proximity correction (MB-OPC) is an essential step in the production of advanced integrated circuits that are manufactured with optical lithography technology. The accuracy of these models depends highly on the experimental data used in the model development (model calibration) process. The calibration features are weighted relative to each other depending on many aspects, this weighting plays an important role in the accuracy of the developed models.
In this paper, the effect of the feature weighting on OPC models is studied. Different weighting schemes are introduced and the effect on both the optical and resist models (specifically the resist model coefficients) is presented and compared. The effect of the weighting on the overall model fitting was also investigated.
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