Impact of pupil plane filtering on mask roughness transfer

Baylav, Burak; Maloney, Chris; Levinson, Zac; Bekaert, Joost; Pret, Alessandro Vaglio; Smith, Bruce W.

doi:10.1116/1.4825102

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Comparisons were made in terms of 3σ LER and power spectral densities (PSD). The details of experimental conditions can be found in elsewhere [4]; however, it is important to point out that the phase filter induced mitigation is dependent on the slit location, as can be seen on Fig. 2 (a) for the roughness amplitude programmed in anticorrelated fashion with an amplitude of 3 nm/edge and frequency of 5/µm.…”

Section: Background Of the Workmentioning

confidence: 99%

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Mitigating mask roughness via pupil filtering

et al. 2014

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The roughness present on the sidewalls of lithographically defined patterns imposes a very important challenge for advanced technology nodes. It can originate from the aerial image or the photoresist chemistry/processing [1]. The latter remains to be the dominant group in ArF and KrF lithography; however, the roughness originating from the mask transferred to the aerial image is gaining more attention [2-9], especially for the imaging conditions with large mask error enhancement factor (MEEF) values. The mask roughness contribution is usually in the low frequency range, which is particularly detrimental to the device performance by causing variations in electrical device parameters on the same chip [10][11][12]. This paper explains characteristic differences between pupil plane filtering in amplitude and in phase for the purpose of mitigating mask roughness transfer under interference-like lithography imaging conditions, where onedirectional periodic features are to be printed by partially coherent sources. A white noise edge roughness was used to perturbate the mask features for validating the mitigation.

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Section: Background Of the Workmentioning

confidence: 99%

“…More detailed information on experimental discussions can be found in [4] for the latter. The remaining modulation in phase type filtering resulted in residual LWR value of 2.7 nm.…”

Section: White Noise Mask Lwr Mitigation Studiesmentioning

confidence: 99%

Mitigating mask roughness via pupil filtering

et al. 2014

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“…It can be measured along a single line scan or along several parallel lines. SWR (in the semiconductor industry) is usually evaluated as the standard deviation of the distance between a reference line [ 29 ]:

where

and n is the total number of data points in the evaluation and

are the data points representing the depth of each point in the servo direction ( y -axis).…”

Section: Sidewall Imaging Afm Systemmentioning

confidence: 99%

Atomic Force Microscopy Sidewall Imaging with a Quartz Tuning Fork Force Sensor

Hussain

Yan

Zhang

et al. 2018

Sensors

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Sidewall roughness measurement is becoming increasingly important in the micro-electromechanical systems and nanoelectronics devices. Atomic force microscopy (AFM) is an emerging technique for sidewall scanning and roughness measurement due to its high resolution, three-dimensional imaging capability and high accuracy. We report an AFM sidewall imaging method with a quartz tuning fork (QTF) force sensor. A self sensing and actuating force sensor is fabricated by microassembling a commercial AFM cantilever (tip apex radius ≤10 nm) to a QTF. The attached lightweight cantilever allows high-sensitivity force detection (7.4% Q factor reduction) and sidewall imaging with high lateral resolution. Owing to its unique configuration, the tip of the sensor can detect sidewall surface orthogonally during imaging, which reduces lateral friction. In experiments, sidewalls of a micro-electro-mechanical system (MEMS) structure fabricated by deep reactive ion etching process and a standard step grating are scanned and the sidewall roughness, line edge roughness and sidewall angles are measured.

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“…[1][2][3] The criticality of pupil characterization continues into EUV lithography (EUVL) with an additional importance placed on understanding the ways that the pupil variation evolves during system operation. 4,5 Interferometric techniques are the de-facto standard of wavefront analysis.…”

Section: Introductionmentioning

confidence: 99%

An automated image-based tool for pupil plane characterization of EUVL tools

Levinson

Smith

Fenger³

et al. 2016

Extreme Ultraviolet (EUV) Lithography VII

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Pupil plane characterization will play a critical role in image process optimization for EUV lithography (EUVL), as it has for several lithography generations. In EUVL systems there is additional importance placed on understanding the ways that thermally-induced system drift affect pupil variation during operation. In-situ full pupil characterization is therefore essential for these tools. To this end we have developed Quick Inverse Pupil (QUIP)-a software suite developed for rapid characterization of pupil plane behavior based on images formed by that system. The software consists of three main components: 1) an image viewer, 2) the model builder, and 3) the wavefront analyzer. The image viewer analyzes CD-SEM micrographs or actinic mask micrographs to measure either CDs or through-focus intensity volumes. The software is capable of rotation correction and image registration with subpixel accuracy. The second component pre-builds a model for a particular imaging system to enable rapid pupil characterization. Finally, the third component analyzes the results from the image viewer and uses the optional pre-built model for inverse solutions of pupil plane behavior. Both pupil amplitude and phase variation can be extracted using this software. Inverse solutions are obtained through a model based algorithm which is built on top of commercial rigorous full-vector simulation software.

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Impact of pupil plane filtering on mask roughness transfer

Cited by 7 publications

References 14 publications

Mitigating mask roughness via pupil filtering

Mitigating mask roughness via pupil filtering

Atomic Force Microscopy Sidewall Imaging with a Quartz Tuning Fork Force Sensor

An automated image-based tool for pupil plane characterization of EUVL tools

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