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The dimensional variations caused by topography differences between active and non active shallow trench isolation (STI) areas, at the gate level, need to be controlled through proper use of reflectivity control methods. Line-width variation caused by topography can either be a disastrous problem or so small that it is hard to detect. Some of the primary variables include the step-height, active-area-width and planarization length of the BARC being used. In order to experimentally compare different reflectivity control methods, wafers were built with steps ranging from 7.5 nm higher to 27 nm lower than the surroundings. Organic BARC thicknesses of 90 and 130 nm were evaluated. Two resist thicknesses were also evaluated. Along with examining the effect of step-height, we also examined the effect of activearea-widths ranging from 0.5 um to 4.5 um. The data demonstrate that line-width variation going over this variety of steps is well under 1 nm when BARC and resist thicknesses are optimized.

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Performing MBOPC (model based Optical Proximity Correction) on layouts is an essential part of patterning advanced integrated circuits. With constantly shrinking CD tolerance and tighter ACLV budgets, the model has to be accurate within a few nanometers. The accuracy of a model in predicting wafer behavior dictates the success of the patterning process. Model calibration is a critical procedure to provide an accurate correlation between design and wafer features. The model calibration process consists of arriving at a variable threshold polynomial as a function of aerial image parameters -intensity maximum (I max ), intensity minimum (I min ), slope, curvature etc. In this paper, a strong correlation between the accuracy of the model and the image parameters is demonstrated. Data from model calibration of two different layers in 65nm technology node will be shown to demonstrate the dependence of model accuracy on aerial image parameters. Data show that accuracy of the model degrades as a function of resolution, i.e. features with low I max , low slope and low contrast are more difficult to model than higher resolution features. During calibration of the model, some parameters can be adjusted to obtain a balance between the model accuracy of weaker and stronger resolution features. Suggestions for improving the accuracy of the weaker features based on an analysis of the image parameters are shown. The correlation between accuracy of the model and image parameters will be useful in limiting OPC corrections on features with poor aerial image quality.

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