Measuring local CD uniformity in EUV vias with scatterometry and machine learning

Kong, Dexin; Schmidt, Daniel; Church, Jennifer; Liu, Chi‐Chun; Breton, Mary; Murray, Cody; Miller, Eric R.; Meli, Luciana; Sporre, J.; Felix, Nelson; Ahsan, Ishtiaq; Cepler, Aron; Cheng, Marjorie; Koret, Roy; Turovets, Igor

doi:10.1117/12.2551498

Cited by 4 publications

(5 citation statements)

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“…It is interesting to note that CD uniformity values, can be extracted from within the measured area using machine learning algorithms. 7 Nonetheless, there is a desire to significantly reduce the spot size area by at least 100×, which would benefit measurements in active areas as well as enable mapping to understand local process variabilities between the center and edge of memory arrays. Because reducing the measurement area of a broadband instrument is not straightforward, the interesting concept of microsphere-assisted spectroscopic reflectometry was recently demonstrated as one option to dramatically reduce the spot size even below the diffraction limit.…”

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

“…Machine learning can enable scatterometry solutions without the need for a geometrical model and may even allow for the prediction of parameters that are inaccessible with traditional model solutions. 7,9,25,26,30 However, the required reference data are not always easy to obtain. It may also be very time-consuming and expensive if, for example, only cross-sectional TEM reference metrology is an option.…”

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

“…It is important to note that, although the nonimaging technique may allow for subnanometer precision, the measured quantity is always an average value from across the entire probed area, which is identical to the focused beam size. It is interesting to note that CD uniformity values, can be extracted from within the measured area using machine learning algorithms 7 . Nonetheless, there is a desire to significantly reduce the spot size area by at least 100×, which would benefit measurements in active areas as well as enable mapping to understand local process variabilities between the center and edge of memory arrays.…”

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

“…Machine learning can enable scatterometry solutions without the need for a geometrical model and may even allow for the prediction of parameters that are inaccessible with traditional model solutions 7 , 9 , 25 , 26 , 30 . However, the required reference data are not always easy to obtain.…”

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

See 3 more Smart Citations

Spectral interferometry for fully integrated device metrology

Schmidt¹,

Medikonda²,

Rizzolo³

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

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A spectral interferometry technique called vertical travelling scatterometry (VTS) is introduced, demonstrated, and discussed. VTS utilizes unique information from spectral interferometry and enables solutions for applications that are infeasible with traditional scatterometry approaches. The technique allows for data filtering related to spectral information from buried layers, which can then be ignored in the optical model. Therefore, using VTS, selective analyses of the topmost part of an arbitrarily complex stack are possible within a single metrology step. This methodology helps to overcome geometrical complexities and allows for focusing on parameters of interest through dramatically simplified optical modeling. Such model simplifications are specifically desired for back-end-of-line applications. Three examples are monitored discussed: (i) the critical dimensions (CDs) of a first metal level on top of nanosheet gate-all-around transistor structures, (ii) the thickness of an interlayer dielectric above embedded memory in the active area, and (iii) the CDs of trenches on top of tall stacks in the micrometer range comprising many layered dielectrics. It was found that, in all three cases, data filtering through VTS allowed for a simple optical model capable of delivering parameters of interest. The validity and accuracy of the VTS solution results were confirmed by extensive reference metrology obtained by traditional scatterometry, scanning electron microscopy, and transmission electron microscopy. Furthermore, it was shown that machine learning models trained with VTS filtered data can converge to a robust solution with a smaller dataset compared with models training with traditional scatterometry data.

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Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

“…Machine learning can enable scatterometry solutions without the need for a geometrical model and may even allow for the prediction of parameters that are inaccessible with traditional model solutions 7 , 9 , 25 , 26 , 30 . However, the required reference data are not always easy to obtain.…”

Section: Scatterometry Challenges and Strategiesmentioning

confidence: 99%

See 2 more Smart Citations

Spectral interferometry for fully integrated device metrology

Schmidt¹,

Medikonda²,

Rizzolo³

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

View full text Add to dashboard Cite

show abstract

“…Kong et al, used optical spectrometry along with ML to obtain the local critical dimension (CD) uniformity of contact hole arrays. The dataset for training the ML algorithm was collected by CD scanning electron microscopy, and the corresponding local CD uniformity was obtained 28 . Nevertheless, to the best of our knowledge, there are no reports on the prediction of photoresist sensitivity using ML in pursuit of developing novel photoresist materials.…”

Section: Introductionmentioning

confidence: 99%

Predicting photoresist sensitivity using machine learning

Ghule,

Kim,

Jang

2023

Bulletin Korean Chem Soc

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We introduce a scheme for predicting photoresist sensitivity using machine learning (ML) work flow on the basis of previously reported experimental data. Different ML models, specifically Linear Regression, Kernel Ridge Regression, Gaussian Process Regressor, Random Forest Regressor, and Multilayer Perceptron Regressor, were evaluated to rapidly identify the best sensitivity prediction model. The experiment was carried out on the Google Colab platform using the Materials Simulation Toolkit for Machine Learning and Sci‐kit Learn. Different ensemble models were utilized without splitting the dataset to determine the prediction accuracy of the ML models. The hyperparameter optimization was established with a 70/30 ratio, followed by a K‐Fold cross‐validation to improve the model prediction performance. The optimized ML model showed a prediction performance of R2 = 0.83, RMSE = 10.53, and MARE = 0.68. Hence, by optimizing the hyperparameters used in the ML model, the sensitivity of the photoresist materials can be predicted with improved prediction performance.

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