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Silicon bandgap limits the reduction of operation voltage when downscaling device sizes. This increases the electrical field within-a-device and hot carrier aging (HCA) is becoming an important reliability issue again for some CMOS technologies. For nanodevices, there are a number of challenges for characterizing their HCA: the random charge–discharge of traps in gate dielectric causes “within-a-device-fluctuation (WDF),” making the parameter shift uncertain after a given HCA. This can introduce errors when extracting HCA time exponents and it will be shown that the lower envelope of the WDF must be used. Nanodevices also have substantial device-to-device variation (DDV) and multiple tests are needed for evaluating their standard deviation ( $\sigma )$ and mean value ( $\mu $ ). Repeating the time-consuming HCA tests is costly and a voltage-step-stress method is applied to reduce the number of tests by 80%. For a given number of devices under tests (DUTs), there is a little information on the accuracy of the extracted $\sigma $ and $\mu $ . We will develop a method to provide this information, based on the defect-centric model. For 40 DUTs with an average of ten traps per device, the extracted $\mu $ and $\sigma $ has an accuracy of ±14% and ±24%, respectively, with a 95% confidence. [ABSTRACT FROM PUBLISHER]

SRAM is vulnerable to device-to-device variation (DDV), since it uses minimum-sized devices and requires device matching. In addition to the as-fabricated DDV at time-zero, aging induces a time-dependent DDV (TDDV). Bias temperature instability (BTI) is a dominant aging process. A number of techniques have been developed to characterize the BTI, including the conventional pulse- \(I\) – \(V\) , random telegraph noises, time-dependent defect spectroscopy, and TDDV accounting for the within-device fluctuation. These techniques, however, cannot be directly applied to SRAM, because their test conditions do not comply with typical SRAM operation. The central objective of this paper is to develop a technique suitable for characterizing both the negative BTI (NBTI) and positive BTI (PBTI) in SRAM. The key issues addressed include the SRAM relevant sensing Vg, measurement delay, capturing the upper envelope of degradation, sampling rate, and measurement time window. The differences between NBTI and PBTI are highlighted. The impact of NBTI and PBTI on the cell-level performance is assessed by simulation, based on experimental results obtained from individual devices. The simulation results show that, for a given static noise margin, test conditions have a significant effect on the minimum operation bias. [ABSTRACT FROM PUBLISHER]

Variability of nanometer-size devices is a major challenge for circuit design. Apart from the as-fabricated variability, the postfabrication degradation introduces a time-dependent variability, originating from statistical distribution of charge location and number. The existing characterization techniques do not always capture the maximum degradation. Some of them does not separate the device-to-device variation from the charging fluctuation within the same device, either. The objective of this paper is to develop a new analysis method for characterizing time-dependent device-to-device variation, accounting for within-device fluctuation (TVF). The TVF captures the maximum degradation, separate device-to-device variation from within-device fluctuation, and reduce the data points by three orders of magnitude. It is shown that the popular data acquisition at discrete time points does not capture the fluctuation well and drain current must be measured continuously. The TVF shows that degradation has two components—a fluctuation with time and one whose discharge is not observed under a given bias. Although both of them increase with stress time, the correlation between them is weak, indicating two different origins. [ABSTRACT FROM AUTHOR]