Statistical analysis of process variation based on indirect measurements for electronic system design

We present a framework for the analysis of process variation across semiconductor wafers. The framework is capable of quantifying the primary parameters affected by process variation, e.g., the effective channel length, which is in contrast with the former techniques wherein only secondary parameters were considered, e.g., the leakage current. Instead of taking direct measurements of the quantity of interest, we employ Bayesian inference to draw conclusions based on indirect observations, e.g., on temperature. The proposed approach has low costs since no deployment of expensive test structures might be needed or only a small subset of the test equipments already deployed for other purposes might need to be activated. The experimental results present an assessment of our framework for a wide range of configurations.