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Extraction of Statistical Gate Oxide Parameters From Large MOSFET Arrays

In modern MOS technologies continuous scaling of the geometry of transistors has led to an increase of the variability between nominally identical devices. To study the variability and reliability of such devices, a statistically significant number of samples needs to be tested. In this work we present a characterization study of defects causing BTI and RTN, performed on custom built arrays consisting of thousands of nanoscale devices. In such nanoscale devices, variability and reliability issues are typically analyzed for individual defects. However, the large number of measurements needed to extract statistically meaningful results make this approach infeasible. To analyze the large set of measurement data, we employ statistical distributions of the threshold voltage shifts arising from defects that capture and emit charge. This allows us to extract defect statistics using a defect-centric approach. Defect distributions are characterized for various gate, drain and bulk biases, and for two geometries to verify the methodology and to obtain statistics suitable for TCAD modeling and lifetime estimation. With the TCAD models we extrapolate the observed degradation of the devices. Finally, we investigate the influence of bulk and drain stress biases on the defects and observe that the impact of bulk bias on the device degradation is similar to that of the gate bias. In contrast, drain stress with drain biases up to −0.45V appears to be negligible for the investigated technology. Our measurements also clearly reveal that the overall BTI degradation is heavily dependent on the gate-bulk stress bias, while the extracted number of RTN defects seems to be independent on stress.