A new method for the in vivo identification of degenerated material property ranges of the human eye: feasibility analysis based on synthetic data.

This paper proposes a new method for in vivo and almost real-time identification of biomechanical properties of the human cornea based on non-contact tonometer data. Further goal is to demonstrate the method's functionality based on synthetic data serving as reference. For this purpose, a finite element model of the human eye is constructed to synthetically generate full-field displacements from different data sets with keratoconus-like degradations. Then, a new approach based on the equilibrium gap method combined with a mechanical morphing approach is proposed and used to identify the material parameters from virtual test data sets. In a further step, random absolute noise is added to the virtual test data to investigate the sensitivity of the new approach to noise. As a result, the proposed method shows a relevant accuracy in identifying material parameters based on full-field displacements. At the same time, the method turns out to work almost in real time (order of a few minutes on a regular workstation) and is thus much faster than inverse problems solved by typical forward approaches. On the other hand, the method shows a noticeable sensitivity to rather small noise amplitudes rendering the method not accurate enough for the precise identification of individual parameter values. However, analysis show that the accuracy is sufficient for the identification of property ranges which might be related to diseased tissues. Thereby, the proposed approach turns out promising with view to diagnostic purposes.

Detection of buried reference structures by use of atomic force acoustic microscopy.

The miniaturization of micro- and nanoelectronic components requires new methods for the inspection of buried inner structures at the nanoscale. We used the atomic force acoustic microscopy technique (AFAM) to image subsurface defects. This technique combines high lateral resolution with the capability to determine local elastic properties of materials near the surface. As the structures buried near the surface change the effective tip-sample contact stiffness it is possible to detect them. For the verification of the detection capabilities of AFAM we fabricated well-defined buried void structures with different geometries and dimensions. Large, thin, plate like structures of silicon nitride with a local filling were our first test samples. Then, sets of nine small, square, thin plates with thicknesses increasing stepwise from 30 to 270 nm were etched in a thinned silicon wafer. The last two samples contained wedge structures of widths varying between 1.6 and 10 µm. Our results showed that it was possible to detect buried void structures at depths between 180 and 900 nm. We also observed that the depths at which the buried defects can be detected by the use of the AFAM method depend on the defect dimensions and geometry, and on the mismatch in the elastic properties of the sample and the defects. The experimental results obtained for the groups of small, thin plates were verified by quantitative analysis via finite element method (FEM) simulations.