Holistic lithography in the age of the Artificial Intelligence of Things

Brink, Martin van den

doi:10.1117/12.2580424

Cited by 3 publications

(1 citation statement)

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“…The overlay error as measured after a plasma etching step is named on-product overlay (OPO), which represents the layer-to-layer connectivity in a geometric sense. Therefore, optimizing OPO is more important than optimizing the scanner overlay error alone, thus calling for a mask-scanner-process co-optimization in the holistic lithography 1 approach.…”

Section: Introductionmentioning

confidence: 99%

Predictive compact model for stress-induced on-product overlay correction

Zhang

Tabery

Maas

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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On-product overlay (OPO) is an important indicator of device yield. In this work, we show that stressed thin films used in semiconductor manufacturing can be an important contributor to OPO at multiple length scales. Depending on the stress level, film thickness, and the mask design, the overlay impact can be a few nanometers for the exposure of the next lithography layer. A predictive compact model based on pattern density is developed to accurately predict this overlay impact. The model is then verified using short-loop dual damascene wafers with stress split. The predictive model opens a new opportunity for model-based mask correction during optical proximity correction to increase the overlay margin for subsequent lithography exposures.

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Section: Introductionmentioning

confidence: 99%

Predictive compact model for stress-induced on-product overlay correction

Zhang

Tabery

Maas

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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The unfolding road from dust to trust

Beiu

2022

Advances in 3OM: Opto-Mechatronics, Opto-Mechanics, and Optical Metrology

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Analysis of mitigating factors for line edge roughness generated during electron beam lithography using machine learning

Jin

Kozawa

Tamura³

2021

Jpn. J. Appl. Phys.

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Machine learning has been reported to be useful for the analysis of the trade-off relationships among major properties of chemically amplified extreme ultraviolet resists. The resist materials and processes used in photomask production using electron beam lithography seem similar. However, they involve distinct processes and factors. As one of the critical issues in resist pattern formation, line edge roughness (LER) was investigated using machine learning based on six variables, namely, sensitivity, half-pitch, exposure pattern width, beam blur, and the concentrations of photoacid generator and photodecomposable quenchers in terms of a chemical gradient (an indicator of LER). The relationship between the chemical gradient and these six variables was well formulated using the 5th degree polynomials of these six variables. The coefficients of feature values indicated that the process blur is a relatively more important factor than the beam blur in the 1.95-3.70 nm range in 11-16 nm half-pitch patterning.

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Holistic lithography in the age of the Artificial Intelligence of Things

Cited by 3 publications

References 0 publications

Predictive compact model for stress-induced on-product overlay correction

Predictive compact model for stress-induced on-product overlay correction

The unfolding road from dust to trust

Analysis of mitigating factors for line edge roughness generated during electron beam lithography using machine learning

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