Dataset for "Spatial periodicities inside the Talbot effect: understanding, control and applications for lithography"

This dataset contains the data used to create the figures within the article "Spatial periodicities inside the Talbot effect: understanding, control and applications for lithography" by Pierre Chausse and Philip Shields. The data comprises one-dimensional and two-dimensional data showing the spatial variation of the light intensity behind grating masks that are illuminated with collimated 375 nm optical radiation. The grating mask period has been varied from 600 nm to 1200 nm.A MATLAB computer model was developed to simulate the operation of a DTL machine in which the light source is a 375 nm UV laser. An optical system generates a plane wave illuminating a conventional lithography mask at normal incidence so that the light arriving at the mask is homogeneous, unpolarised, and in phase. The complex electric field is derived using Fast Fourier Transform (FFT) of the electric field of the mask. As periodic masks are mandatory for DTL, periodic boundary conditions can be applied. Contributions from the different mask regions, propagating behind it, are given amplitudes of 1 and 0 for a chrome amplitude mask, and 1 and -1 for a phase mask. The integration of the three-dimensional light known as the Talbot carpet is then performed. The electric field is multiplied by its conjugate to obtain the surface light intensity.The simulations were performed within MATLAB.Within the main .rar file, each figure has its own compressed .rar file. In each of these, the final figure in .jpg is included, along with the source figure or subfigures in both .tif and MATLAB proprietary .fig formats. The underlying data has also been exported in CSV format. In the data folder for figure 1, there are two CSV files: - x.csv corresponds to the x-axis value for each curve; - y.csv contains the values of the different spatial periodicity, where NaN means no value. For all the other figures, each subfigure is accompanied by two or three CSV files: - x.csv is the x-axis points value; - y.csv is the y-axis points value; - Data.csv corresponds to the matrix data, where applicable