## Depth-probe

A depth-probe simulation is an auxiliary simulation type, which helps to visualize the total intensity as function of the beam incidence angle and the position in the sample.

Here we will consider the intensity map produced by a neutron resonator composed of one Ti/Pt bilayer.

A more detailed description of this example can be found in the Depth Probe Tutorial.

In the figure above, the $y$ axis corresponds to the position across the sample surface (in nanometers), while the $x$ axis corresponds to the incident angle $\alpha_i$. The script below provides a complete example of how to run a depth-probe simulation which produces the image above.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106  #!/usr/bin/env python3 """ Basic example of depth-probe simulation with BornAgain. Sample structure: ----------------------- inf Si ----------------------- 0 nm Ti ----------------------- -13 nm Pt ----------------------- -45 nm Ti ----------------------- -55 nm TiO2 ----------------------- -58 nm D2O ----------------------- -inf Beam comes from silicon side. z axis is directed up and perpendicularly to the sample, z = 0 corresponding to the sample surface """ import bornagain as ba from bornagain import angstrom, deg, nm, nm2, kvector_t # layer thicknesses in angstroms t_Ti = 130.0*angstrom t_Pt = 320.0*angstrom t_Ti_top = 100.0*angstrom t_TiO2 = 30.0*angstrom # beam data ai_min = 0.0*deg # minimum incident angle ai_max = 1.0*deg # maximum incident angle n_ai_bins = 5000 # number of bins in incident angle axis beam_sample_ratio = 0.01 # beam-to-sample size ratio wl = 10*angstrom # wavelength in angstroms # convolution parameters d_ang = 0.01*ba.deg # spread width for incident angle n_points = 25 # number of points to convolve over n_sig = 3 # number of sigmas to convolve over # depth position span z_min = -100*nm z_max = 100*nm n_z_bins = 500 def get_sample(): """ Constructs a sample with one resonating Ti/Pt layer """ # Define materials material_D2O = ba.HomogeneousMaterial("D2O", 0.00010116, 1.809e-12) material_Pt = ba.HomogeneousMaterial("Pt", 0.00010117, 3.01822e-08) material_Si = ba.HomogeneousMaterial("Si", 3.3009e-05, 0.0) material_Ti = ba.HomogeneousMaterial("Ti", -3.0637e-05, 1.5278e-08) material_TiO2 = ba.HomogeneousMaterial("TiO2", 4.1921e-05, 8.1293e-09) # Define layers layer_1 = ba.Layer(material_Si) layer_2 = ba.Layer(material_Ti, 13.0*nm) layer_3 = ba.Layer(material_Pt, 32.0*nm) layer_4 = ba.Layer(material_Ti, 10.0*nm) layer_5 = ba.Layer(material_TiO2, 3.0*nm) layer_6 = ba.Layer(material_D2O) # Define sample sample = ba.MultiLayer() sample.addLayer(layer_1) sample.addLayer(layer_2) sample.addLayer(layer_3) sample.addLayer(layer_4) sample.addLayer(layer_5) sample.addLayer(layer_6) return sample def get_simulation(sample): """ Returns a depth-probe simulation. """ alpha_distr = ba.DistributionGaussian(0.0, d_ang) footprint = ba.FootprintSquare(beam_sample_ratio) simulation = ba.DepthProbeSimulation() simulation.setBeamParameters(wl, n_ai_bins, ai_min, ai_max, footprint) simulation.setZSpan(n_z_bins, z_min, z_max) simulation.addParameterDistribution("*/Beam/InclinationAngle", alpha_distr, n_points, n_sig) simulation.setSample(sample) return simulation if __name__ == '__main__': import ba_plot sample = get_sample() simulation = get_simulation(sample) ba_plot.run_and_plot(simulation) 
DepthProbe.py