On the chemical composition of TiAlN thin films - Comparison of ion beam analysis and laser-assisted atom probe tomography with varying laser pulse energy
Source abstract
Abstract
We compare the chemical composition of TiAlN thin films determined by ion beam analysis and laser-assisted atom probe tomography (APT). The laser pulse energy during APT was increased subsequently from 10 to 20, 30, 40, 50, 100 and 200 pJ within a single measurement, covering the range that is typically employed for the analysis of transition metal nitrides. The laser pulse energy-dependent Ti, Al and N concentrations were compared to ion beam analysis data, combining Rutherford backscattering spectrometry (RBS) and elastic recoil detection analysis (ERDA) with the total measurement uncertainty of 2.5% relative deviation. It can be learned that the absolute N concentration from APT is underestimated by at least 5.5 at.% (up to 8.2 at.%) and the absolute Al concentration from APT is overestimated by at least 4.5 at.% (up to 6.2 at.%), while absolute Ti concentration values are for both techniques in good agreement with maximum deviations <2 at.%. Hence, the here presented comparative analysis clearly shows that absolute Al and N concentration values obtained by ion beam analysis deviate significantly to the APT data for the laser pulse energy range from 10 to 200 pJ. Possible causes for the compositional discrepancy between Rutherford backscattering spectrometry/elastic recoil detection analysis and APT, such as molecular ions, multiple detection events and preferential evaporation/retention of species with different evaporation fields, are discussed. The presented data emphasize that laser-assisted APT is a precise tool to quantify the chemical composition of TiAlN thin films, that lacks accuracy.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Materials Characterization Techniques · Metal and Thin Film Mechanics · Diamond and Carbon-based Materials Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (40)
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