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Plasma-assisted direct bonding has been investigated for wafer scale encapsulation of microelectromechanical systems ͑MEMS͒. Direct bonding requires smooth and flat wafer surfaces, which is seldom the case after fabrication of MEMS devices. Therefore, we have used polished chemical vapor deposited oxide as an intermediate bonding layer. The oxide layer is polished prior to bonding the MEMS wafer to cap silicon wafer. The bonding is carried out with plasma-assisted direct wafer bonding at a low temperature ͑Ͻ300°C͒. Two different methods to form electrical contacts to the encapsulated device are presented. In the first method trenches are etched on the surface of the cap wafer before the bonding. During the bonding the trenches are aligned to the contact pads of the device wafer. After bonding the cap wafer is thinned down with grinding until the path to the contact pads is opened. In the second method one or both of the wafers are thinned down to around 100 m after bonding. The electrical path to contact pads is formed using V-groove sawing, metal sputtering, and lithography. To test the viability of the developed methods for MEMS encapsulation, we have sealed polysilicon resonator structures at a wafer level.

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Medical information retrieval suffers from a dual problem: users struggle in describing what they are experiencing from a medical perspective and the search engine is struggling in retrieving the information exactly matching what users are experiencing. We demonstrate interactive symptom elicitation for diagnostic information retrieval. Interactive symptom elicitation builds a model from the user's initial description of the symptoms and interactively elicitates new information about symptoms by posing questions of related, but uncertain, symptoms for the user. As a result, the system interactively learns the estimates of symptoms while controlling the uncertainties related to the diagnostic process. The learned model is then used to rank the associated diagnoses that the user might be experiencing. Our preliminary experimental results show that interactive symptom elicitation can significantly improve user's capability to describe their symptoms, increase the confidence of the model, and enable effective diagnostic information retrieval.