The Influence of Adjacent Segment on the Reliability of Cu Dual Damascene Interconnects

Three terminal ‘dotted-I’ interconnect structures, with vias at both ends and an additional via in the middle, were tested under various test conditions. Mortalities (failures) were found in right segments with jL value as low as 1250 A/cm, and the mortality of a dotted-I segment is dependent on the direction and magnitude of the current in the adjacent segment. Some mortalities were also found in the right segments under a test condition where no failure was expected. Cu extrusion along the delaminated Cu/Si₃N₄ interface near the central via region was believed to cause the unexpected failures. From the time-to-failure (TTF), it is possible to quantify the Cu/Si₃N₄ interfacial strength and bonding energy. Hence, the demonstrated test methodology can be used to investigate the integrity of the Cu dual damascene processes. As conventionally determined critical jL values in two-terminal via-terminated lines cannot be directly applied to interconnects with branched segments, this also serves as a good methodology to identify the critical effective jL values for immortality.

Contextual models for object detection using boosted random fields

We seek to both detect and segment objects in images. To exploit both local image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph structure and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection performance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes.

The Early Detection of Motion Boundaries

This thesis shows how to detect boundaries on the basis of motion information alone. The detection is performed in two stages: (i) the local estimation of motion discontinuities and of the visual flowsfield; (ii) the extraction of complete boundaries belonging to differently moving objects. For the first stage, three new methods are presented: the "Bimodality Tests,'' the "Bi-distribution Test,'' and the "Dynamic Occlusion Method.'' The second stage consists of applying the "Structural Saliency Method,'' by Sha'ashua and Ullman to extract complete and unique boundaries from the output of the first stage. The developed methods can successfully segment complex motion sequences.