One of the most important aspects of any manufacturing effort is the yield of the process. Today, the investment in facilities, equipment and materials is so high that consistently high yields are vital to the profitability of the semiconductor manufacturer. Furthermore, the engineers must get to that consistent high yield as quickly as possible to avoid product delays.
Solving a yield issue requires complex pattern recognition skills using limited amounts of data. While fab tools and test equipment generate terabytes of data, knowing what to look for in that mountain of data is a difficult task. Typically, one needs to start with a big picture understanding, by identifying what is failing (failure mode of the IC), and how the failure manifests itself in the process (spatial dependencies, lot dependencies, equipment dependencies, etc.). The engineer must then formulate hypotheses that fit the "what" and "how". He or she would then look through the data at hand to try and confirm or deny each hypothesis. If there is insufficient data to do this, one may have to gather additional data. Finally, if there is no data on hand that can conclusively prove an hypotheses, then the engineer would typically submit a sample of ICs for failure analysis to help provide greater understanding of the problem. Once the engineering team identifies the source of the problem, they will develop a fix and implement the fix on a set of control material. This course walks through the basics of the yield analysis process, including yield models, methods to visualize yield-related data, and techniques for solving yield-related issues.