On Extracting Reliability Information from Speed Binning

Adaptive Voltage Frequency Scaling (AVFS) is an important means to overcome process-induced variability challenges for advanced high-performance circuits. AVFS requires and allows determining the maximum speed F
max
(V
dd
) reachable under a set of certain operation voltages V
dd
. In this paper, it is shown that the F
max
(V
dd
) measurements contain relevant data to identify some hidden defects in a chip which are reliability threats and can cause device failures, but pass the speed binning procedure within the given specifications.Static Timing Analysis (STA) is applied to a circuit designed by using standard cell libraries in which the underlying transistors along with process variations have been carefully calibrated against industrial 14nm FinFET measurement data, and in-stances with and without injected small resistive open defects are generated. From the slope of the function F
max
(V
dd
), a machine learning procedure can identify some defects with high precision and few false positives. These chips can be then discarded without any further need and cost for testing. It has to be noted that this reliability information comes for free from the data which is already generated, and does not need any additional measurements.