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To predict the negative bias temperature instability (NBTI) toward the end of pMOSFETs’ ten years lifetime, power-law-based extrapolation is the industrial standard method. The prediction accuracy crucially depends on the accuracy of time exponents, n. n reported by early work spreads in a wide range and varies with measurement conditions, which can lead to unacceptable errors when extrapolated to ten years. The objective of this paper is to find how to make n extraction independent of measurement conditions. After removing the contribution from as-grown hole traps, a new method is proposed to capture the generated defects (GDs) in their entirety. n extracted by this method is around 0.2 and insensitive to measurement conditions for the four fabrication processes we tested. The model based on this method is verified by comparing its prediction with measurements. Under ac operation, the model predicts that the GD can contribute to ~90% of NBTI at ten years. [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]

Defects in gate oxide cause breakdown and shorten device lifetime. Early works mainly focused on generation process that converts a precursor into a charged defect. Although it can be neutralized through “recovery,” the defect is still there and will recharge when resuming stress. Recently, we have shown that this is not always the case and some defects can be lost, but a detailed investigation is missing. The central objective of this work is to accurately define and extract the loss, separate it from slowdown, and evaluate their enhancement of device lifetime. Loss is defined as elimination of defects, while slowdown means that recharging a “hardened” precursor takes longer than charging a fresh one. Clear evidences show that losses originate from permanent components, i.e., generated interface states and antineutralization positive charges, while slowdown occurs to both permanent and recoverable components. Loss is thermally accelerated, but slowdown of recoverable component is not. This improved understanding adds slowdown and losses to the existing framework for defects. For the first time, we report that the losses and slowdown enhance device lifetime by a factor of 2.6–4.3, for an allowed threshold voltage shift of 20–45 mV. [ABSTRACT FROM AUTHOR]