MEMS microstructured surface characterisation based on wavelet transform techniques

Fibre interferometry holds many advantages for the online measurement of high precision surfaces. However, the method requires stabilising against path length change due to temperature drift and vibration. A method of stabilising such an interferometer using intensity feedback was examined and shown to be ineffective at low frequencies. A second method, using real-time calculated phase feedback was also investigated, and was observed to be more robust.
This paper investigates wavelet theory and techniques for the extraction of morphological structures from micro/nano scalar surfaces. The basic conceptions of Micro-Electro-Mechanical Systems (MEMS) and some typical microstructured surface such as MEMS-based sensors, hard disk surfaces, micro-lens arrays and the chip pin surfaces is are introduced. A novel complex wavelet model is then presented, the complex wavelet model can be used in solving the problems of surface texture with microstructured features, in which small shifts of the input signal can cause large variations in the distribution of energy between wavelet coefficients at different scales. As a result, micro channel and step features on a surface topography can be identified and extracted with refined accuracy. A case study is conducted using MEMS-based sensors to demonstrate the application of using the new wavelet model in the assessment of surface topography.