Impact of mask three-dimensional effects on resist-model calibration

Abstract

We report on a comparison between a fullphysical resist model that was calibrated to experimental line/space ͑L/S͒ critical dimension ͑CD͒ data under the flat-mask ͑also called "thin-mask" or "Kirchhoff"͒ approximation with the model obtained when using a mask 3-D calculation engine ͑i.e., one that takes into account the masktopography effects͒. Both models were tested by evaluating their prediction of the CDs of a large group of 1-D and 2-D structures. We found a clear correlation between the measured-predicted CD difference and the magnitude of the mask 3-D CD effect, and show that the resist model calibrated with a mask 3-D calculation engine clearly offers a better CD predictability for certain types of structures.Calibration of full-physical resist models 1-4 has a long record of use and is constantly being refined to offer better predictability to rigorous simulators, such as Sentaurus Lithography™ of Synopsys or Prolith™ of KLA-Tencor. We used a large experimental critical dimension ͑CD͒ data set, which was generated for the purpose of optical proximity correction ͑OPC͒-model building, to assess the importance of mask 3-D ͑or mask-topography͒ effects in resist-model calibration. The CD data was measured with a top-down Hitachi H9380 scanning electron microscope from a 120 nm TOK TArF-Pi6-001-ME resist on 95 nm ARC29SR Barc on Si wafer exposed with an NA= 1.20 ASML XT:1700i immersion scanner, using cQuad20 outer / inner = 0.96/ 0.60 XY-polarized illumination. The experimental CD data consisted of two types. The first type consisted of Bossung ͑i.e., through-dose and -focus͒ data for 30 L/S structures, with pitches between 100 and 400 nm. The resist models were essentially calibrated using ͑part of͒ these L/S Bossungs only. The second CD-data group ͑ϳ5000 different structures͒ was measured at a single dose-focus setting only and was used for verifying the resist models. It consisted of more 1-D-type data ͑L/S structures with and without SRAFs, isolated lines and trenches and line and trench doublets and triplets͒, end-of-line ͑EOL͒ gap-CD data and of CDs measured from more complicated 2-D structures ͑which we shall call the Generic 2-D or G2D structures͒. Most of the structures in this data set are located on the mask in a locally clear-field area, but others are located in a locally dark-field area. The resist-model quality is then quantified by first calculating the measured-predicted CD difference, ⌬CDϵ CDគ measured-CDគsimulated, for all individual verification structures. In view of the very large number of structures in the verification set ͑ Ͼ 5000͒, we reduced this ⌬CD data set by calculating the mean value ͓Mean͑⌬CD͔͒ and the standard deviation ͓StDev͑⌬CD͔͒ for a number of-somewhat arbitrarily chosen-subsets of this verification-structure group.In Fig. 1, we represent such a verification result, by plotting these Mean͑⌬CD͒ values for each of the selected structure subsets ͑the labels we use for these subsets are explained in the caption of Fig. 1͒. The error bars in Fig. 1͑b͒ actually stand for the ...

Calibration of physical resist models: methods, usability, and predictive power

“…However, for dark field structures (inverted 2 bar and 3 bar features) the 2D-mask model exhibits significantly higher RMSD values (> 8 nm) which is in-line with what has been reported also in [6]. In contrast, the dark field structures of the 3D-mask model achieved consistently lower RMSD values around 1 nm to 4 nm depending on the specific dark field pattern type.…”

“…field patterns, the RMSD values for dark field structures are unacceptably large (8 nm -25 nm) for the 2D-mask resist models that have been calibrated on bright field structures only. In reference [6] we show that this alone cannot account for the discrepancy for the dark-field structures, but that the mask-3D effects play an important role here. Nevertheless, the question as to how much the resist model predictions based on the Kirchhoff mask approximation can be improved by including additionally dark field patterns into the calibration data set is an interesting one and needs to be investigated.…”

“…It has been shown [6] that 3D-mask effects significantly impact the simultaneous predictability of bright and dark field patterns. While resist models based on a full 3D-mask description show a very good predictability of both, bright field and dark Fig.…”

“…Also, off-axis illumination is resulting in large oblique incident angles onto the mask, which even further emphasizes mask topography effects. In a recent paper by De Bisschop et al the impact of 3D-mask effects on resist model calibration has been assessed for the first time [6]. In the following, we present some additional calibration specific details on the impact of the 3D-mask effect.…”

Challenges in process marginality for advanced technology nodes and tackling its contributors

“…Defect Critical Resist Profile: Processing at physical limits increases the probability of defects [5]. Increased attention is therefore given to both defect detection and defect classification both in optical rule check (ORC) before mask tapeout as well as during ramp up/pre-production phase to investigate and correct a defect (hot-spot).…”