CDS&E: Computational Tools for Visualizing SIMS Depth Profiling Data in Three Dimensions

With the support of the Chemical Measurement and Imaging Program in the Division of Chemistry, Professor Mary Kraft of the University of Illinois Urbana-Champaign is developing new computational visualization and analysis tools for depth profiling secondary ion mass spectrometry (SIMS) data. The distributions of various molecules within a sample influence its properties and function. Thus, knowledge of the molecular distribution within a sample facilitates understanding the material’s functions, and ultimately, designing new functional materials with advanced capabilities. Three-dimensional (3D) images of the molecular distributions within a sample may be acquired with depth profiling secondary ion mass spectrometry (SIMS). However, the 3D SIMS images acquired from contoured samples are distorted along the z-axis, and this distortion hinders their interpretation. Professor Kraft and her team plan to tackle this problem by developing software tools that use only SIMS depth profiling data to correct this distortion and produce accurate 3D images of component distribution within contoured samples. This research will entail the development of computational strategies for extracting height information from secondary ion intensities collected from organic and inorganic materials, accuracy assessment algorithms, and user-friendly interfaces that facilitate usage of these tools by others in academia and industry. The proposed research will produce new software tools that enable the user to create accurate component-specific 3D SIMS images of contoured organic and inorganic samples using any SIMS depth profiling dataset. These new computational tools would improve both the accuracy of SIMS 3D depth profiling images and their interpretation. Ultimately, this research could increase understanding of structure-function relationships in biological and synthetic materials, which could enable the design of new materials with advanced functions. The project will train graduate and undergraduate students in materials characterization using SIMS depth profiling, MATLAB coding, computational data visualization, image processing, image interpretation, semiconductor fabrication and characterization, cell culture, and polymer synthesis. This training will prepare students for careers in our semiconductor, electronics and pharmaceutical sectors. The computational tools and SIMS depth profiling datasets generated by this research will serve as STEM education tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.