Sample Representation


Reflectometry samples consist of 1-D stacks of layers joined by error function interfaces. The layers themselves may be uniform slabs, or the scattering density may vary with depth in the layer. The first layer in the stack is the substrate and the final layer is the surface. Surface and substrate are assumed to be semi-infinite, with any thickness ignored.


The interface between layers is assumed to smoothly follow and error function profile to blend the layer above with the layer below. The interface value is the 1-\(\sigma\) gaussian roughness. Adjacent flat layers with zero interface will act like a step function, while positive values will introduce blending between the layers.

Blending is usually done with the Nevot-Croce formalism, which scales the index of refraction between two layers by \(\exp(-2 k_n k_{n+1} \sigma^2)\). We show both a step function profile for the interface, as well as the blended interface.


The blended interface representation is limited to the neighbouring layers, and is not an accurate representation of the effective reflectivity profile when the interface value is large relative to the thickness of the layer.

We will have a mechanism to force the use of the blended profile for direct calculation of the interfaces rather than using the interface scale factor.


Materials can be stacked as slabs, with a thickness for each layer and roughness at the top of each layer. Because this is such a common operation, there is special syntax to do it, using ‘|’ as the layer separator and () to specify thickness and interface. For example, the following is a 30 Å gold layer on top of silicon, with a silicon:gold interface of 5 Å and a gold:air interface of 2 Å:

>> from refl1d import *
>> sample = silicon(0,5) | gold(30,2) | air
>> print sample
Si | Au(30) | air

Individual layers and stacks can be used in multiple models, with all parameters shared except those that are explicitly made separate. The syntax for doing so is similar to that for lists. For example, the following defines two samples, one with Si+Au/30+air and the other with Si+Au/30+alkanethiol/10+air, with the silicon/gold layers shared:

>> alkane_thiol = Material('C2H4OHS',bulk_density=0.8,name='thiol')
>> sample1 = silicon(0,5) | gold(30,2) | air
>> sample2 = sample1[:-1] | alkane_thiol(10,3) | air
>> print sample2
Si | Au(30) | thiol(10) | air

Stacks can be repeated using a simple multiply operation. For example, the following gives a cobalt/copper multilayer on silicon:

>> Cu = Material('Cu')
>> Co = Material('Co')
>> sample = Si | [Co(30) | Cu(10)]*20 | Co(30) | air
>> print sample
Si | [Co(30) | Cu(10)]*20 | Co(30) | air

Multiple repeat sections can be included, and repeats can contain repeats. Even freeform layers can be repeated. By default the interface between the repeats is the same as the interface between the repeats and the cap. The cap interface can be set explicitly. See model.Repeat for details.

Freeform layers

Freeform profiles allow us to adjust the shape of the depth profile using control parameters. The profile can directly represent the scattering length density as a function of depth (a FreeLayer), or the relative fraction of one material and another (a FreeInterface). With a freeform interface you can simultaneously fit two systems which should share the same volume profile but whose materials have different scattering length densities. For example, a polymer in deuterated and undeuterated solvents can be simultaneously fit with freeform profiles.

We have multiple representations for freeform profiles, each with its own strengths and weaknesses:

At present, monotone cubic interpolation is the most developed, but work on all representations is in flux. In particular not every representation supports all features, and the programming interface may vary. See the documentation for the individual models for details.

Comparison of models

There are a number of issues surrounding the choice of model.

  • How easy is it to bound the profile values

    If the you can put reasonable bounds on the control points, then the user can bring to bear prior information to limit the search space. For example, it is common to add an unknown silicon-oxide profile to the surface of silicon, with SLD varying between the values for Si and SiO2.

  • How easy is it to edit the profile interactively

    Given a representation of the freeform layer, we want to be able to plot control points that you can drag in order to change the shape of the profile.

  • Is the profile stable or does it oscillate wildly

    Many systems are best described by smoothly varying density profiles. If the profile oscillates wildly it makes the search for optimal parameters more difficult.

  • Can you change the order of interpolation and preserve the profile

    While the current code does not support it, we would like to be able to select the freeform profile order automatically, using the minimum order we can to achieve \(\chi^2 = 1\), and rejecting profiles which overfit the data. For now this is done by hand, performing fits with different orders independently, but there are likely to be speed gains by first fitting coarse models with low Q then adding detail to the profile while adding additional Q values.

  • Is the representation unique? Are the control parameters strongly correlated?

    Fitting and uncertainty analysis benefit from unique solutions. If the model representation is matched by a family of parameters it is more difficult to interpret the results of the uncertainty analysis or to get convergence from the parameter refinement engine.

Monotone cubic interpolation is the easiest to control. The value of the interpolating polynomial lies mostly within the range of the control points, and the profile goes through the control points. This means you can set up bounds on the control parameters that limit the profile to a certain range of scattering length densities in a region of the profile. It also leads to a very intuitive interactive profile editor since the control points can be moved directly on profile view. However, although the profile is \(C^1\) smooth everywhere, the \(C^2\) transitions can be abrupt at the control points. Better algorithms for selecting the gradient exist but have not been implemented, so this may improve in the future.

Parametric B-splines are commonly used in computer graphics because they create pleasing curves. The interpolating polynomial lies within the convex hull of the control points. Unfortunately the distance between the curve and the control point can be large, and this makes it difficult to set reasonable bounds on the values of the control points. One can reformulate the interpolation so that control points lie on the curve and still preserve the property of pleasing curves, but this can lead to wild oscillations in the profile when the control points become too close together. While the natural representation can be used in an interactive profile editor, the fact that the control points are sometimes far away from the profile makes this inconvenient. The complementary representation is used in programs such as Microsoft Excel, with the control point directly on the curve and a secondary control point to adjust the slope at that control point.

Chebyshev interpolating polynomials are a near optimal representation for an function over an interval with respect to the maximum norm. The interpolating polynomial is a weighted sum \(\sigma_{i=0}^n c_i T_i(z)\) of the Chebyshev basis polynomials \(T_i\) with Chebyshev coefficients \(c_i\). One very interesting property is that the lower order coefficients remain the same has higher order interpolation polynomials are constructed. This makes the Chebyshev polynomials very interesting candidates for a freeform profile fitter which selects the order of the profile as part of the fit. Chebyshev interpolating polynomials can exhibit wild oscillations if the coefficients become large, so the smoothness can be somewhat controlled by limiting these higher values, but we have not explored this in depth. The Chebyshev coefficient values are not directly tied to the profile, so there is no intuitive way to directly control the coefficients in an interactive editor. The complementary representation uses the profile value at the chebyshev nodes for specific positions \(z_i\) on the profile. This representation is much more natural for an interactive editor, but some choices of control values will lead to wild oscillations between the nodes. Similarly the complementary representation is unsuitable as a representation for the fittable parameters since the bounds on the parameters do not directly limit the range of possible values of the profile.

Future work

We only have polynomial spline representations for our profiles. Similar profiles could be constructed from different basis functions such as wavelets, the idea being to find a multiscale representation of your profile and use model selection techniques to determine the most coarse grained representation that matches your data.

Totally freeform representations as separately controlled microslab heights would also be interesting in the context of a maximum entropy fitting engine: find the smoothest profile which matches the data, for some definition of ‘smooth’. Some possible smoothness measures are the mean squared distance from zero, the number of sign changes in the second derivative, the sum of the absolute value of the first derivative, the maximum flat region, the minimum number of flat slabs, etc. Given that reflectometry inversion is not unique, the smoothness measure must correspond to the likelihood of finding the system in that particularly state: that is, don’t expect your sample to show zebra stripes unless you are on an African safari or visiting a zoo.