Reference-free grazing incidence x-ray fluorescence and reflectometry as a methodology for independent validation of x-ray reflectometry on ultrathin layer stacks and a depth-dependent characterization

Abstract

Nanolayer stacks are technologically very relevant for current and future applications in many fields of research. A non-destructive characterization of such systems is often performed using X-ray reflectometry (XRR). For complex stacks of multiple layers, low electron density contrast materials or very thin layers without any pronounced angular minima, this requires a full modeling of the XRR data. As such modeling is using the thicknesses, the densities and the roughnesses of each layer as parameters, this approach quickly results in a large number of free parameters. In consquence, cross-correlation effects or interparameter dependencies can falsify the modeling results. Here, we present a route for validation of such modeling results which is based on the reference-free grazing incidence X-ray fluorescence (GIXRF) methodology. In conjunction with the radiometrically calibrated instrumentation of the Physikalisch-Technische Bundesanstalt the method allows for reference-free quantification of the elemental mass depositions. In addition, a modeling approach of reference-free GIXRF-XRR data is presented, which takes advantage of the quantifiable elemental mass depositions by distributing them depth dependently. This approach allows for a reduction of the free model parameters. Both the validation capabilities and the combined reference-free GIXRF-XRR modeling are demonstrated using several nanoscale layer stacks consisting of HfO 2 and Al 2 O 3 layers.

Shape- and Element-Sensitive Reconstruction of Periodic Nanostructures with Grazing Incidence X-ray Fluorescence Analysis and Machine Learning

“…As we have employed radiometrically calibrated X-ray fluorescence (XRF) instrumentation, we can perform reference-free GIXRF [ 32 ] and gain a quantitative access to the elemental mass depositions present on the sample [ 33 ]. At each angular position for and , a fluorescence spectrum is recorded with a calibrated silicon drift detector (SDD) [ 34 ] and the incident photon flux is monitored by means of a calibrated photodiode.…”

Shape- and element-sensitive reconstruction of periodic nanostructures with grazing incidence X-ray fluorescence analysis and machine learning

“…As we have employed radiometrically calibrated Xray fluorescence (XRF) instrumentation, we can perform reference-free GIXRF [30] and gain a quantitative access to the elemental mass depositions present on the sample [31]. At each angular position for θ and ϕ, a fluorescence spectrum is recorded with a calibrated silicon drift detector (SDD) [32] and the incident photon flux is monitored by means of a calibrated photodiode.…”

Grazing incidence x-ray fluorescence based characterization of nanostructures for element sensitive profile reconstruction

“…The elemental mass depositions can be quantified in a reference-free approach 26 using the atomic fundamental parameters, describing the process of fluorescence production and the known instrumental parameters (e.g., the incident photon flux and the solid angle of detection). 27…”