Correlating system test fmax with structural test fmax and process monitoring measurements

System test has been the standard measurement to evaluate performance variability of high-performance microprocessors. The question of whether or not many of the lower-cost alternative tests can be used to reduce system test has been studied for many years. This paper utilizes a data-learning approach for correlating three test datasets, structural test, ring oscillator test, and scan flush test, with system test. With the data-learning approach, higher correlation can be found without altering test measurements or test conditions. Rather, the approach utilizes new optimization algorithms to extract more useful information in the three test datasets, with particular success using the structural test data. To further minimize test cost, process monitoring measurements (ring oscillator and scan flush tests) are used to reduce the need for high-frequency structural test. We demonstrate our methodology on a recent high-performance microprocessor design.