Effect of Cluster Ion Analysis Fluence on Interface Quality in SIMS Molecular Depth Profiling

Published

November 10, 2009

Author(s)

Christopher Szakal, Steven Hues, J Greg Gillen, Joe Bennett

Abstract

Cluster ion beams are being routinely utilized in secondary ion mass spectrometry (SIMS) for molecular depth profiling analyses, including the generation of three-dimensional data sets. Certain sample targets, such as soft organic matrices, often require large analysis ion fluences in order to generate sufficient count rates for pixel-to-pixel contrast. However, in the generation of this data during a SIMS dual-beam experiment, little attention has been given to the effects imparted into a depth-profiled sample merely by acquiring data between sputter erosion cycles. We find that the amount of Bin+ analysis fluence within a dual-beam sputter depth profiling experiment is not negligible and can degrade the interface widths of a high-quality PMMA film on silicon, despite the use of sputter beams such as SF5+ and C60+ to remove accumulated analysis beam-induced damage. These Bin+ fluence levels, which are often needed for generating enough counts in 3D molecular imaging experiments, can degrade the interface to the point where depth profiling information may not truly reflect the concentration of targeted molecules vs depth. This degradation is expected to worsen for multiple organic layer systems, where the accumulation of ion beam-induced damage can increase to levels where depth-resolved chemistry may not be achievable.

Citation

The Journal of Physical Chemistry C

Pub Type

Journals

Download Paper

https://doi.org/10.1021/jp905019x

Local Download

Keywords

cluster SIMS, molecular depth profiling, interface, SF5, C60, Bi3

Citation

Szakal, C. , Hues, S. , Gillen, J. and Bennett, J. (2009), Effect of Cluster Ion Analysis Fluence on Interface Quality in SIMS Molecular Depth Profiling, The Journal of Physical Chemistry C, [online], https://doi.org/10.1021/jp905019x, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=901692 (Accessed December 12, 2024)