Reconstructing the surface microtopography through data fusion from a range of microscopies offers a way to provide considerably more information than any one technique alone. This thesis explores several methods for combining the info
rmation from different kinds of microscopes including height data, electron images and optical images.
Each microscopy has distinct advantages and disadvantages that can be considered in terms of data leakage in the transfer function from the actual sampl
e topography to any individual dataset. These leakage paths are identified for a range of microscopies including atomic force microscopy, scanning electron microscopy and white light interferometry. Existing techniques for reconstruction from multiple image datasets, such as shape-from-shading and stereomicroscopy are evaluated, again in terms of information leakage. A new approach is presented which attempts to minimize leakage by identifying a pathway of data fusion which first isolates the most reliable information in each dataset, and then maintains this information as the datasets are combined. The identification of the most trusted data requires knowledge of each imaging mechanism, while the final reconstruction is based on iterative matching between the datasets and a simulated image. Hence the final reconstruction incorporates both the datasets from the microscopes and their imaging physics to suppress data leakage.
This approach is first validated with a model micromachined sample using a combination of height interferometer data together with back-scattered and secondary scanning electron microscope images. It is then applied to particulate samples to demonstrate the capabilities of the approach.