Spatial correlation modeling for probe test cost reduction in RF devices

Test cost reduction for RF devices has been an ongoing topic of interest to the semiconductor manufacturing industry. Automated test equipment designed to collect parametric measurements, particularly at high frequencies, can be very costly. Together with lengthy set up and test times for certain measurements, these cause amortized test cost to comprise a high percentage of the total cost of manufacturing semiconductor devices. In this work, we investigate a spatial correlation modeling approach using Gaussian process models to enable extrapolation of performances via sparse sampling of probe test data. The proposed method performs an order of magnitude better than existing spatial sampling methods, while requiring an order of magnitude less time to construct the prediction models. The proposed methodology is validated on manufacturing data using 57 probe test measurements across more than 3,000 wafers. By explicitly applying probe tests to only 1% of the die on each wafer, we are able to predict probe test outcomes for the remaining die within 2% of their true values.