CD prediction by threshold energy resist model (TERM)

Yoo, Jeseon; Kwon, Yeong-Keun; Park, Jun-Taek; Sohn, Dong-Soo; Kim, Sang-Gon; Sohn, Young-Soo; Oh, Hye−Keun

doi:10.1117/12.474510

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…describes the relationship between E and I th [43,44,54]. Because the refraction index of the photoresist film is not 1, the focus error should be scaled by a factor β Z .…”

Section: B Vlim Calibrationmentioning

confidence: 99%

True process variation aware optical proximity correction with variational lithography modeling and model calibration

Shi

Pan

2007

J. Micro/Nanolith. MEMS MOEMS

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Optical proximity correction (OPC) is one of the most widely used Resolution Enhancement Techniques (RET) in nanometer designs to improve subwavelength printability. Conventional model-based OPC assumes nominal process conditions without considering process variations because of the lack of variational lithography models. A simple method to improve OPC results under process variations is to sample multiple process conditions across the process window, which requires long runtime. We derive a variational lithography model (VLIM) which can simulate across the process window without much runtime overhead compared to the conventional lithography models. To match the model to experimental data, we demonstrate VLIM calibration method. The calibrated model has accuracy comparable to non-variational models, but it has the advantage of taking process variations into consideration. We introduce the variational edge placement error (V-EPE) metrics based on the model, a natural extension to the edge placement error (EPE) used in conventional OPC algorithms. A true process-variation aware OPC (PV-OPC) framework is proposed using the V-EPE metric. Due to the analytical nature of VLIM, our PV-OPC is only about 2-3× slower than the conventional OPC, but it explicitly considers the two main sources of process variations (exposure dose and focus variations) during OPC. Thus our post PV-OPC results are much more robust than the conventional OPC ones, in terms of both geometric printability and electrical characterization under process variations.

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“…describes the relationship between E and I th [43,44,54]. Because the refraction index of the photoresist film is not 1, the focus error should be scaled by a factor β Z .…”

Section: B Vlim Calibrationmentioning

confidence: 99%

True process variation aware optical proximity correction with variational lithography modeling and model calibration

Shi

Pan

2007

J. Micro/Nanolith. MEMS MOEMS

View full text Add to dashboard Cite

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“…It is based on the hypothesis that the resist has a threshold-like behavior [7,8] . This kind of relation has to be used because the aerial image CD are expressed as a function of aerial image intensity, while experimental data are expressed as a function of exposure dose.…”

Section: Diffused Aerial Image Modelmentioning

confidence: 99%

Validity of the diffused aerial image model: an assessment based on multiple test cases

2003

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Lithography modeling is a very attractive way to predict the critical dimensions of patterned features after lithographic processing. In a previous paper [1] , we have presented the assessment of three different simplified resist models (aerial image model, aerial image convolved with fixed gaussian noise and aerial image convolved with variable gaussian noise) by using a systematic comparison between experimental and simulated data. It has been shown that the aerial image convolved with fixed gaussian noise, or "diffused aerial image model" (DAIM), exhibits surprisingly good results of CD prediction for lines @ 193nm: using these datasets, the DAIM appears as a fast and accurate model for CD prediction. This approach allows also an easy run, and because it needs only four adjustable parameters, it avoids the difficult task of resist parameters extraction associated to full resist models, with. In this paper, we enlarge the datasets used for the assessment of the DAIM by considering both lines and contact holes of various sizes printed at different wavelengths. The reference wafers have been printed at 248nm, 193nm and 157 nm. The procedure used to extract the model parameters has been improved and now needs less data to provide acceptable values. We will show that the validity of the DAIM extends well outside the results presented in ref.1. Experimental data printed using various wavelengths, resists and exposure tools can be simulated accurately with CD prediction error ranging within few percents. It is to be noted that the results that will be presented on contact holes data indicate that the model is valid for 2D features. Finally, a comparison with full resist models shows that the accuracy of DAIM is comparable to more sophisticated and heavier models .

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“…First, the basic equation, which links simulated intensity thresholds and experimental exposure doses, is an empirical one. Using the hypothesis that the resist has a threshold-like behavior [9,10] , the threshold is tied to the exposure dose by the relation: threshold const dose = In fact, the dose is never ideally determined because of dose measurement offset. Taking into account this dose offset, we chose the hypothesis that:…”

Section: Unambiguous Relation Between Experimental Doses and Simulated mentioning

confidence: 99%

Assessment of different simplified resist models

2002

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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.

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CD prediction by threshold energy resist model (TERM)

Cited by 3 publications

References 4 publications

True process variation aware optical proximity correction with variational lithography modeling and model calibration

True process variation aware optical proximity correction with variational lithography modeling and model calibration

Validity of the diffused aerial image model: an assessment based on multiple test cases

Assessment of different simplified resist models

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