The Storing Matter technique applied to Alq3 : influence of the collector material and the sputter-deposition energy on fragmentation

Enhancing ToF-SIMS OLED Data Analysis with Neural Networks and Mathematical Spectral Mixing

This study presents a method employing artificial neural networks (ANN) for automated interpretation and depth profiling of organic multilayers using a limited set of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra. To overcome the challenges of acquiring massive data sets for OLEDs, training data was generated by combining existing ToF-SIMS data sets with mathematically generated spectra. The classification model achieved an impressive 99.9% accuracy in identifying the mixed layers of the OLED dyes. The study demonstrates the synergy of ToF-SIMS and ANN analysis for effective classification and depth profiling of the OLED layers, providing valuable insights for the development and optimization of organic electronic devices.

Optimizing the sputter deposition process of polymers for the Storing Matter technique using PMMA

Quantitative analyses in secondary ion mass spectrometry (SIMS) become possible only if ionization processes are controlled. The Storing Matter technique has been developed to circumvent this so-called matrix effect, primarily for inorganic samples, but has also been extended to organic samples. For the latter, it has been applied to polystyrene in order to investigate the extent of damage in the polymer, its fragmentation during the sputter deposition process and the effect of the deposition process on the spectra taken by Time-of-Flight SIMS (ToF-SIMS). In this work, a multi-technique approach, which employs the Storing Matter technique for deposition and ToF-SIMS and X-ray photoelectron spectroscopy for characterization, is used to enhance the control of the deposition process, including the thickness of the deposit, the alteration of the source film and the influence of polymer composition on the Storing Matter process. Poly (methyl methacrylate) (PMMA) is used for this work. More detailed information about the sticking of polymer fragments on the metal collector is obtained by density functional theory calculations. This work allows for the conclusion that a part of the fragments deposited on the collector surface diffuses on the latter, reacts and recombines to form larger fragments. The behaviour observed for PMMA is similar to polystyrene, showing that oxygen has no major influence on the processes occurring during the sputter deposition process. Additionally, we have developed a new methodology using 2D ToF-SIMS images of the deposit to monitor the deposit thickness and to identify surface contaminations. The latter are not only located at the position of the deposit but all over the collector surface. Copyright © 2016 John Wiley & Sons, Ltd.

Investigation of the depth-profiling capabilities of the Storing Matter technique

The so-called Storing Matter technique allows the matrix effect observed in secondary ion mass spectrometry to be successfully circumvented. We therefore investigate in this work the depth-profiling capabilities of the Storing Matter technique with a goal of developing protocols for quantitative depth profiles. The effect of the steps involved in the Storing Matter process on the main parameters such as the depth resolution and the dynamic range is studied experimentally and by simulations. A semi-automated process consisting of the sputter-deposition process on a rotating collector in the Storing Matter instrument followed by a complete analysis of the collector by secondary ion mass spectrometry is defined. This protocol is applied to depth profile a B implant in Si and a Sn/Zn multilayered sample, and the results are compared with those obtained with conventional secondary ion mass spectrometry.