STATISTICAL SIGNIFICANCE APPROACH FOR DIFFERENT WORK FUNCTION MODELING OF SEMICONDUCTOR

Significances of the work function in the field of surface science need knowledge of how electrons are transported out of the Fermi surface to the vacuum as well as ways the energy needed can be accurately estimated. Full knowledge of work functions as well as the surface energy of the metallic surface enhances understanding of the formation of grain boundaries and surface segregation. Statistical significance of differences in modeling approaches requires studies that can reliably distinguish between systematic approach effects and errors resulting from modeling approach variation. In this work, researchers introduced analysis of variance and correlation analysis to assess the statistical significance of differences in modeling approach variation. Comparisons of obtained results with estimating the work function of semiconductor variation data of support vector machine (SVM) and linear regression with natural logarithm transformation (LRNLT) were presented.