Facilitating Mechanical Design with Augmented Reality

By enhancing a real scene with computer generated objects, Augmented Reality (AR), has proven itself as a valuable Human-Computer Interface (HCI) in numerous application areas such as medical, military, entertainment and manufacturing. It enables higher performance of on-site tasks with seamless presentation of up-to-date, task-related information to the users during the operation. AR has potentials in design because the current interface provided by Computer-aided Design (CAD) packages is less intuitive and reports show that the presence of physical objects help design thinking and communication. This research explores the use of AR to improve the efficiency of a design process, specifically in mechanical design.

A Two-level Prediction Model for Deep Reactive Ion Etch (DRIE)

We contribute a quantitative and systematic model to capture etch non-uniformity in deep reactive ion etch of microelectromechanical systems (MEMS) devices. Deep reactive ion etch is commonly used in MEMS fabrication where high-aspect ratio features are to be produced in silicon. It is typical for many supposedly identical devices, perhaps of diameter 10 mm, to be etched simultaneously into one silicon wafer of diameter 150 mm. Etch non-uniformity depends on uneven distributions of ion and neutral species at the wafer level, and on local consumption of those species at the device, or die, level. An ion–neutral synergism model is constructed from data obtained from etching several layouts of differing pattern opening densities. Such a model is used to predict wafer-level variation with an r.m.s. error below 3%. This model is combined with a die-level model, which we have reported previously, on a MEMS layout. The two-level model is shown to enable prediction of both within-die and wafer-scale etch rate variation for arbitrary wafer loadings.