Resist modeling is an attractive way to predict the critical dimensions of patterned features after lithographic processing. Unfortunately, previous works have shown that model parameters are very difficult to determine and have often a poor range of validity outside the dataset that have been used to generate them [1,2] . The goal of this work is to assess different simplified resist models using a systematic method. We have studied the accuracy of aerial image model and aerial image plus gaussian noise convolution model. The approach is based on the comparison between simulated and experimental data for periodic lines of various dimensions at various illumination conditions. We also propose a reliable expression for Bossung curves fitting. Using simple physical considerations, the expression has been made very simple and efficient. After a proper setting of the model parameters to the experimental data, mean CD discrepancies between simulation and experiment are as small as 5% and can be 3% for certain feature types. Moreover, we show that simple gaussian noise convolution models can be predictive with the same accuracy. The method for CD prediction is fully described in this paper. Significant improvements have been made in resists modeling over the last several years, but simplified resist models such as "aerial image + gaussian noise " seems to be an effective tool for CD prediction, which remains the major demand of IC manufacturers.
With the scaling down of integrated circuit devices, a constant effort is needed to improve the patterning technologies of interconnect stacks using either the metallic masking strategy or the organic masking strategy. Critical dimensions and profile control, plasmainduced damages (modifications, post etch residues, porous SiOCH roughening) are the key challenges to successfully pattern dual damascene porous SiOCH structures.We have compared the patterning performances of both masking strategies in terms of profile control. One of the main challenges is to optimize the plasma processes to minimize the dielectric sidewall modification. This has been achieved by using optimized or new characterization techniques such as scatterometric porosimetry, infrared spectroscopy, x-ray photoelectron spectroscopy.
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