Practical resist model calibration

Baluswamy, Pary; Weatherly, Amy; Kewley, Dave; Brooker, Peter; Pauzer, Mike

doi:10.1117/12.485339

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Calibration of full-physical resist models [1][2][3][4] has a long record of use and is constantly being refined to offer better predictability to rigorous simulators, such as Sentaurus Li-thography™ 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.…”

mentioning

confidence: 99%

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

Bisschop

Muelders

Klostermann

et al. 2009

J. Micro/Nanolith. MEMS MOEMS

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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 ...

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mentioning

confidence: 99%

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

Bisschop

Muelders

Klostermann

et al. 2009

J. Micro/Nanolith. MEMS MOEMS

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“…After calibration, model accuracy should be checked against new data [7]. Our first validation uses identical illumination, film stack, and resist processes as the calibration data, but replaces 1D mask structures with 2D mask structures.…”

Section: -1 Model Validation Using the Calibration Process Conditionsmentioning

confidence: 99%

A calibrated photoresist model for pattern prediction

Hung

Liao

Shih

et al. 2008

Advances in Resist Materials and Processing Technology XXV

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Aerial image metrics and non-rigorous resist modeling have been used in computational lithography to estimate the resist CD on the wafer, with varying degrees of success. However, the complex illumination and optical-proximity correction strategies used at the 90 nm node and below demand accurate simulation at the wafer level, allowing engineers to make informed decisions when optimizing the process. A calibrated, rigorous model simulating the physical-chemistry of exposure, reaction-diffusion and development can produce highly-accurate predictions, even when extrapolated. The advantage of the rigorous physical approach is that simulations retain predictive accuracy even when the experimental conditions are modified.In this work, the rigorous physical resist model available in PROLITH is calibrated for a commercially available ArF resist using top-down CDSEM data, including focus-exposure matrices, CD vs. mask pitch and mask-error enhancement data. All calibration data consist of 1D mask structure e.g. lines and spaces. It will be shown that the resulting resist model, while calibrated using 1D mask structures, is able to accurately predict the performance of 2D mask structures with an area fidelity metric of 98.34% under the illumination conditions used for calibration. It will also be shown that, additionally, the calibrated resist model is able to predict 2D patterns robustly under alternate illumination schemes without the need for re-calibration. The resist model accurately describes the experimental results, validating the extrapolated model predictions. Accurate, calibrated models can be used to improve the process with much less impact on time and resources than empirical methods i.e. wafer fabrication and inspection.

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“…where , and the parameters given by available literature [17][18][19][20][21] in Table 1, the etch rate can be calculated from eqs. (16) and (17).…”

Section: Simulations and Experimentsmentioning

confidence: 99%

Numerical study on photoresist etching processes based on a cellular automata model

Zhou

Huang

et al. 2007

SCI CHINA SER E

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For the three-dimensional (3-D) numerical study of photoresist etching processes, the 2-D dynamic cellular automata (CA) model has been successfully extended to a 3-D dynamic CA model. Only the boundary cells will be processed in the 3-D dynamic CA model and the structure of "if-else" description in the simulation program is avoided to speed up the simulation. The 3-D dynamic CA model has found to be stable, fast and accurate for the numerical study of photoresist etching processes. The exposure simulation, post-exposure bake (PEB) simulation and etching simulation are integrated together to further investigate the performances of the CA model. Simulation results have been compared with the available experimental results and the simulations show good agreement with the available experiments. cellular automata, process simulation, optical lithography simulation, modelAs the micro-electro-mechanical systems (MEMS) design and fabrication become increasingly advanced, accurate three-dimensional (3-D) simulation systems for fabrication processes such as etching, lithography, deposition processes, etc. demonstrate to be useful to improve the efficiency of MEMS design and to reduce the development costs of MEMS products. At the same time, 3-D optical lithography simulation becomes significantly important for the accurate analysis of complex structures, such as contacts, corners, etc., and for the optimization of the masks for these structures, as the complexity of integrated circuits (IC) increases [1] . Furthermore, simulation is essential to determine the best way of pushing the present set of equipment involved in optical lithography to its maximum limits [2] .The photoresist etching simulation is usually the most time-consuming step among the basic optical lithography simulation steps [3] , so the key to increase the speed of optical lithography simulation is to speed up the simulation of the photoresist etching process. During the past two decades, some simulators for optical lithography processes have been successfully developed

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Practical resist model calibration

Cited by 4 publications

References 5 publications

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

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

A calibrated photoresist model for pattern prediction

Numerical study on photoresist etching processes based on a cellular automata model

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