The semiconductor industry faces significant challenges in extracting and leveraging data to improve yield. One of the Defectivity workshop's missions is to locate and identify physical defects on production wafers caused by the process. These defects can directly impact the product electrical performance, highlighting the need for a better understanding of the correlation between defectivity levels (defect count per wafer) and the process involved. Highly complex, those processes are driven by multiple parameters, which means their relationship to defectivity is a major enabler for process tuning toward yield improvement and cost optimization.

In this case study, we demonstrate the efficiency of JMP as a tool for data management and formatting from process in line collection through the following steps:

1. Visualizing the initial extracted data to verify a hypothesis about the worst process tool.
2. Manipulating and extending the data set for a more in-depth analysis of a previously identified process parameter, enabling correlation analysis.
3. Quantifying dollar gains based on the analysis.

Finally, a clear results display is highly beneficial for management when considering a potential process change and making informed decisions regarding the cost-yield balance.