Abstract： In the product validation process of a Field Programmable Gate Array (FPGA) device, cross-factored characterization testing should be prescribed to estimate the effect of long-term process drifting. Process capability is usually estimated on the basis of one normal distribution. But the calculation cannot be applied to the cross-factored experiment data directly. This paper introduces a novel modeling approach to estimate the process capability of FPGAs. A statistical model is built to predict the worst-case values and estimate the inherent process variation, then calculate process capability indices. Therefore, the process capability indices are not obtained on the basis of one normal distribution. They actually show the worst process capability based on the cross-factored experiment data. An automated tool coded with JMP scripting language was also developed to optimize the model automatically, and to standardize the data process. This method can be applied not only to the data in the FPGA product validation process, but to all similar data in the semiconductor industry.