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As CMOS scales down, hot carrier aging (HCA) scales up and can be a limiting aging process again. This has motivated re-visiting HCA, but recent works have focused on accelerated HCA by raising stress biases and there is little information on HCA under use-biases. Early works proposed that HCA mechanism under high and low biases are different, questioning if the high-bias data can be used for predicting HCA under use-bias. A key advance of this work is proposing a new methodology for evaluating the HCA-induced variation under use-bias. For the first time, the capability of predicting HCA under use-bias is experimentally verified. The importance of separating RTN from HCA is demonstrated. We point out the HCA measured by the commercial SourceMeasure-Unit (SMU) gives erroneous power exponent. The proposed methodology minimizes the number of tests and the model requires only 3 fitting parameters, making it readily implementable.

Selector device is critical in high-density cross-point resistive switching memory arrays for suppressing the sneak leakage current path. GexSe1-x based ovonic threshold switch (OTS) selectors have recently demonstrated strong performance with high on-state current, nonlinearity and endurance. Detailed study of its reliability is still lacking and the understanding on the responsible mechanisms is limited. In this work, for the first time, the endurance degradation mechanism of Ge-rich GexSe1-x OTS is identified. Accumulation of slow defects that remain delocalized at off-state and GeSe segregation/crystallization during cycling lead to the recoverable and non-recoverable leakage current, respectively. Most importantly, a refreshing program scheme is developed to recover and prevent the OTS degradation and the endurance can be therefore improved by more than five orders without adding additional material elements or process steps.

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Introduction:The gap between modelling and real performance has been identified as a major constraint for design optimisation [1] and the inaccuracy of NBTI models contributes to it. When developing a NBTI model, short-term accelerated tests are usually used to extract model parameters and it is a common practice to 'qualify' a model by showing it fits well with test data. The models 'qualified' in this way, such as the reaction-diffusion (R-D) framework [2], cannot predict the long-term NBTI under low use-bias for both SiON and HKMG processes (Figs.1a&b) (Fig.2), needed for dynamic voltage scaling power management [6]. The model needs only three fitting parameters. We emphasize that the A-G model is extracted from the accelerated short DC tests and the test data at low biases in lower panels of Fig.1c&d were not used for fitting.