Measurement and Characterisation of Micro/nano-scale Structured Surfaces

Micro/nano-scale structured surfaces play a critical role in precision engineering. For Microelectromechanical Systems (MEMS), they are key factors to ensure the system's functional performance. However, the measurement and characterisation of micro/nano-scale structured surface is still a great challenge for metrologists. As the size of structured surface features is in the range of nm-pm, the traditional measurement methods are no longer available for the micro/nano-scale structured surfaces. The reason is that the conventional measurement instruments often cannot reach the precision of the required measurement task. Moreover, the conventional characterisation and evaluation methods are not applicable for the micro/nano-scale structured surfaces due to their unique characteristics compared to macro engineered surfaces. Therefore, theoretical research of measurement and characterisation for micro/nano-scale structured surfaces needs to be carried out to meet the requirements for future inspection instruments.

The aim of this thesis is to establish a practical measurement guide and develop a methodology for characterisation and evaluation of micro/nano scale structured surfaces. The presented thesis has reviewed the definitions and classifications of structured surfaces. Their most significant applications in MEMS have been introduced. Measurement methods for structured surfaces based on different principles have been investigated. Measurement instruments employed throughout the research of the project have been summarized. To improve the evaluation efficiency, a new classification is given based on the surfaces' micro feature characteristics. Datum planes for structured surfaces have been established. Surface data pre processing, including data enhancement and denoising techniques have been developed. To extract the primary form of the structured surface, a novel feature extraction algorithm based on active contours has been developed and compared with low-level feature extraction techniques. For micro structured steps, evaluation parameters and methods have been investigated with corresponding case studies.