AFM‐Based Hamaker Constant Determination with Blind Tip Reconstruction. Issue 1 (11th August 2022)

• Record Type: Journal Article
• Authors: Ku, Benny; van de Wetering, Ferdinandus; Bolten, Jens; Stel, Bart; van de Kerkhof, Mark A.; Lemme, Max C.
• Is Part Of: Advanced materials technologies. Volume 8:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-11
• Subjects: adhesion force -- atomic force microscope (AFM) -- blind tip reconstruction -- extreme UV photomask particle contamination -- Hamaker theory; Materials science -- Periodicals; Technological innovations -- Periodicals; Materials science; Technological innovations; Periodicals; 620.1105
• DOI: 10.1002/admt.202200411
• Languages: English
• ISSNs: 2365-709X

Abstract

Abstract: Particle contamination of extreme ultraviolet (EUV) photomasks is one of the numerous challenges in nanoscale semiconductor fabrication, since it can lead to systematic device failures when disturbing patterns are projected repeatedly onto wafers during EUV exposure. Understanding adhesion of particle contamination is key in devising a strategy for cleaning of photomasks. In this work, particle contamination is treated as a particle‐plane problem in which surface roughness and the interacting materials have major influences. For this purpose, vacuum atomic force microscopy (AFM) contact measurements to quantify the van der Waals (vdW) forces between tip and sample are performed. This as a vacuum AFM‐based methodology that combines numerical Hamaker theory and blind tip reconstruction is introduced. The Hamaker constants of 15 × 10 −20 J and 13 × 10 −20 J for the material systems of a silicon (Si) tip with both aluminum oxide (Al2 O3 ) and native silicon dioxide (SiO2 ) on Si substrates, respectively have been determined. These methodology allows an alternative, quick and low‐cost approach to characterize the Hamaker constant within the right order of magnitude for any material combination. Abstract : This paper presents an alternative methodology for van der Waals force prediction of particles with any body‐shape and surface roughness. It is optimized for AFM measurements and combines numerical Hamaker theory with blind tip reconstruction. The quantification of interacting forces can lead to more efficient contamination particle removal, especially for EUV photomask cleaning in the semiconductor industry.