This is the fifth in a series of white papers providing a summary of the discussions and future directions that are derived from these topical meetings. This paper focuses on issues related to analysis and visual analytics. While these two topics are distinct, there are clear overlaps between the two. It is common to use different visualizations during analysis and given the sheer volume of social media data, visual analytic tools can be important during analysis, as well as during other parts of the research lifecycle. Choices about analysis may be informed by visualization plans and vice versa - both are key in communicating about a data set and what it means. We also recognized that each field of research has different analysis techniques and different levels of familiarity with visual analytics. Putting these two topics into the same meeting provided us with the opportunity to think about analysis and visual analytics/visualization in new, synergistic ways.

Challenges of the new nanometer processes have complicated the yield enhancement process. The systematic yield loss component is increasing, due to the complexity and density of the new processes and the designs that are developed for them. High product yields can now only be achieved when process failure rates are on the order of a few parts per billion structures. Traditional yield ramping techniques cannot ramp yields to these levels and new methods are required.This paper presents a new systematic approach to yield loss pareto generation. The approach uses a sophisticated Design-of-Experiments (DOE) approach to characterize systematic and random yield loss mechanisms in the Back End Of the Line (BEOL). Sophisticated Characterization Vehicle (CV)™ test chips, fast electrical test and Automatic Defect Localization (ADL) are critical components of the method. Advanced statistical analysis and visualization of the detected and localized electrical defects provides a comprehensive view of the yield loss mechanisms. In situations where the defects are not visible in a SEM of the structure surface, automated FIB and imaging is used to characterize the defect. The combined approach provides the required resolution to appropriately characterize parts per billion failure rates.