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Proceedings Paper

Holistic feedforward control for the 5 nm logic node and beyond
Author(s): Henry Megens; Ralph Brinkhof; Igor Aarts; Haico Kok; Leendertjan Karssemeijer; Gijs ten Haaf; Shawn Lee; Daan Slotboom; Chris de Ruiter; Irina Lyulina; Simon Huisman; Stefan Keij; Evert Mos; Wim Tel; Manouk Rijpstra; Emil Schmitt-Weaver; Kaustuve Bhattacharyya; Robert Socha; Boris Menchtchikov; Michael Kubis; Jan Mulkens
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Paper Abstract

Multi-patterning lithography for future technology nodes in logic and memory are driving the allowed on-product overlay error in an DUV and EUV matched machine operation down to values of 2 nm and below. The ASML ORION alignment sensor provides an effective way to deal with process impact on alignment marks. In addition, optimized higher order wafer alignment models combined with overlay metrology based feedforward correction schemes are deployed to control the process induced overlay variability from wafer-to-wafer and lot-to-lot. In addition machine learning based algorithms based on hybrid metrology inputs, strengthen the control capabilities for high volume manufacturing. The increase of the number of process layers in semiconductor devices results in an increase of control complexity of the total overlay and alignment control strategy. This complexity requires a holistic solution approach, that addresses total overlay optimization from process design, to process setup, and process control in high volume manufacturing. We find the optimum combination between feedforward and feedback, by having feedback deal with constant and predictable parts of overlay and have scanner wafer alignment covering the wafer-to-wafer variable part of overlay. In this paper we present investigation results using more wavelengths for wafer alignment and show the benefits in wavelength selection and recipe optimization. We investigate the wafer-to-wafer variable content of two experiment cases and show that a sample scheme of about 60 marks is well capable estimating the model parameters describing the grid. Finally, we show initial results of using level sensor metrology data as hybrid input to the derivation of the exposure grid.

Paper Details

Date Published: 20 March 2019
PDF: 13 pages
Proc. SPIE 10961, Optical Microlithography XXXII, 109610K (20 March 2019); doi: 10.1117/12.2515449
Show Author Affiliations
Henry Megens, ASML Netherlands B.V. (Netherlands)
Ralph Brinkhof, ASML Netherlands B.V. (Netherlands)
Igor Aarts, ASML Brion Technologies (United States)
Haico Kok, ASML Netherlands B.V. (Netherlands)
Leendertjan Karssemeijer, ASML Netherlands B.V. (Netherlands)
Gijs ten Haaf, ASML Netherlands B.V. (Netherlands)
Shawn Lee, ASML Brion Technologies (United States)
Daan Slotboom, ASML Netherlands B.V. (Netherlands)
Chris de Ruiter, ASML Netherlands B.V. (Netherlands)
Irina Lyulina, ASML Netherlands B.V. (Netherlands)
Simon Huisman, ASML Netherlands B.V. (Netherlands)
Stefan Keij, ASML Netherlands B.V. (Netherlands)
Evert Mos, ASML Netherlands B.V. (Netherlands)
Wim Tel, ASML Netherlands B.V. (Netherlands)
Manouk Rijpstra, ASML Netherlands B.V. (Netherlands)
Emil Schmitt-Weaver, ASML Netherlands B.V. (Netherlands)
Kaustuve Bhattacharyya, ASML Netherlands B.V. (Netherlands)
Robert Socha, ASML Brion Technologies (United States)
Boris Menchtchikov, ASML Brion Technologies (United States)
Michael Kubis, ASML Netherlands B.V. (Netherlands)
Jan Mulkens, ASML Netherlands B.V. (Netherlands)

Published in SPIE Proceedings Vol. 10961:
Optical Microlithography XXXII
Jongwook Kye; Soichi Owa, Editor(s)

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