Dr. Apoorva Oak Profile

Dr. Apoorva Oak

at imec

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 26 August 2024 Paper

Exploring high-NA EUV mask specification for logic metal

Kenichi Miyaguchi, Soobin Hwang, Apoorva Oak, Ryoung-han Kim

Proceedings Volume 13177, 131770G (2024) https://doi.org/10.1117/12.3035717

KEYWORDS: Logic, Metals, SRAF, Design, Source mask optimization, Semiconducting wafers, Extreme ultraviolet, Critical dimension metrology, Printing, Optical lithography

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Proceedings Article | 10 April 2024 Presentation + Paper

Study of EUV stochastic defect on wafer yield

Yi-Pei Tsai, Chieh-Miao Chang, Yi-Han Chang, Apoorva Oak, Darko Trivkovic, Ryoung-Han Kim

Proceedings Volume 12954, 1295404 (2024) https://doi.org/10.1117/12.3010858

KEYWORDS: Stochastic processes, Extreme ultraviolet, Semiconducting wafers, Data modeling, Photomasks, Critical dimension metrology, Optical lithography, Back end of line, Inspection, Extreme ultraviolet lithography

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Proceedings Article | 10 April 2024 Presentation + Paper

Manufacturing-friendly curvilinear standard cell design

Ryoung-Han Kim, Apoorva Oak, Yasser Sherazi, Gioele Mirabelli, Soobin Hwang, Kiho Yang, Hsinlan Chang

Proceedings Volume 12954, 1295405 (2024) https://doi.org/10.1117/12.3009888

KEYWORDS: Design, Metals, Optical proximity correction, Semiconductors, Optical lithography, Photomasks, Logic, Standards development, Manufacturing, Transistors

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Proceedings Article | 28 April 2023 Presentation + Paper

Quantifying process sensitivities for EUV and high-NA using machine learning based analytics

Apoorva Oak, Ryan Ryoung-Han Kim, Soobin Hwang

Proceedings Volume 12495, 124951C (2023) https://doi.org/10.1117/12.2658912

KEYWORDS: Line edge roughness, Extreme ultraviolet, Monte Carlo methods, Artificial neural networks, Double patterning technology, Optical lithography, Machine learning, Optical proximity correction, Error analysis

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Proceedings Article | 13 June 2022 Presentation

Quantifying process parameter impact on edge placement error using machine learning based analytical engine

Apoorva Oak

Proceedings Volume 12052, 1205209 (2022) https://doi.org/10.1117/12.2615607

KEYWORDS: Error analysis, Machine learning, Monte Carlo methods, Neural networks, Manufacturing, Analytics

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Proceedings Article | 26 February 2021 Presentation + Paper

28nm pitch single exposure patterning readiness by metal oxide resist on 0.33NA EUV lithography

D. De Simone, L. Kljucar, P. Das, R. Blanc, C. Beral, J. Severi, N. Vandenbroeck, P. Foubert, A. Charley, A. Oak, D. Xu, W. Gillijns, J. Mitard, Z. Tokei, M. van der Veen, N. Heylen, L. Teugels, Q. T. Le, F. Schleicher, P. Leray, K. Ronse, Il Hwan Kim, Insung Kim, Changmin Park, Jisun Lee, Koungmin Ryu, P. De Schepper, J. Doise, M. Kocsis

Proceedings Volume 11609, 116090Q (2021) https://doi.org/10.1117/12.2584713

KEYWORDS: Extreme ultraviolet lithography, Optical lithography, Oxides, Metals, Extreme ultraviolet, Scanners, Scanning electron microscopy, Photoresist processing, Lithography, Line edge roughness

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For many years traditional 193i lithography has been extended to the next technology node by means of multi-patterning techniques. However recently such a 193i technology became challenging and expensive to push beyond the technology node for complex features that can be tackled in a simpler manner by the Extreme UltraViolet Lithography (EUVL) technology. Nowadays, EUVL is part of the high-volume manufacturing device landscape and it has reached a critical decision point where one can push further the single print on 0.33NA full field scanner or move to a EUV double patterning technology with more relaxed pitches to overcome current 0.33NA stochastic limits. In this work we have selected the 28nm pitch dense line-space (P28) as critical decision check point. We have looked at the 0.33NA EUV single print because it is more cost effective than 0.33NA EUV double patterning. In addition, we have conducted a process feasibility study as P28 in single print is close to the resolution limit of the 0.33NA EUV full field scanner. We present the process results on 28nm dense line-space patterning by using Inpria’s metal-oxide (MOx) EUV resist. We discuss the lithographic and etching process challenges by looking at resist sensitivity, unbiased line edge roughness (LER) and nano patterning failures after etching (AE), using broad band plasma (BBP) and e-beam (EB) defectivity inspection tools. To get further understanding on the P28 single patterning capability we have integrated the developed EUV MOx process in a relevant iN7 technology test vehicle by developing a full P28 metallization module with ruthenium. In such a way we were able to carry on electrical tests on metallized serpentine, fork-fork and tip-to-tip structures designed with a purpose of enabling further learning on pattern failures through electrical measurements. Finally, we conclude by showing the readiness of P28 single exposure using Inpria’s MOx process on a 0.33NA EUV full field scanner.