In the recent years there has been an increasing interest in manufacturing products where surface topography plays a functional role. These surfaces are called engineered surfaces and are used in a variety of industries like semi conductor, data storage, micro-optics, MEMS etc. Engineered products are designed, manufactured and inspected to meet a variety of specifications such as size, position, geometry and surface finish to control the physical, chemical, optical and electrical properties of the surface. As the manufacturing industry strive towards shrinking form factor resulting in miniaturization of surface features, measurement of such micro and nanometer scale surfaces is becoming more challenging. Great strides have been made in the area of instrumentation to capture surface data, but the area of algorithms and procedures to determine form, size and orientation information of surface features still lacks the advancement needed to support the characterization requirements of R&D and high volume manufacturing. This dissertation addresses the development of fast and intelligent surface scanning algorithms and methodologies for engineered surfaces to determine form, size and orientation of significant surface features. Object recognition techniques are used to identify the surface features and CMM type fitting algorithms are applied to calculate the dimensions of the features. Recipes can be created to automate the characterization and process multiple features simultaneously. The developed methodologies are integrated into a surface analysis toolbox developed in MATLAB environment. The deployment of the developed application on the web is demonstrated.
