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Abstract The recharge oscillator (RO) is a simple mathematical model of the El Niño Southern Oscillation (ENSO). In its original form, it is based on two ordinary differential equations that describe the evolution of equatorial Pacific sea surface temperature and oceanic heat content. These equations make use of physical principles that operate in nature: (a) the air‐sea interaction loop known as the Bjerknes feedback, (b) a delayed oceanic feedback arising from the slow oceanic response to winds within the equatorial band, (c) state‐dependent stochastic forcing from fast wind variations known as westerly wind bursts (WWBs), and (d) nonlinearities such as those related to deep atmospheric convection and oceanic advection. These elements can be combined at different levels of RO complexity. The RO reproduces ENSO key properties in observations and climate models: its amplitude, dominant timescale, seasonality, and warm/cold phases amplitude asymmetry. We discuss the RO in the context of timely research questions. First, the RO can be extended to account for ENSO pattern diversity (with events that either peak in the central or eastern Pacific). Second, the core RO hypothesis that ENSO is governed by tropical Pacific dynamics is discussed from the perspective of influences from other basins. Finally, we discuss the RO relevance for studying ENSO response to climate change, and underline that accounting for ENSO diversity, nonlinearities, and better links of RO parameters to the long term mean state are important research avenues. We end by proposing important RO‐based research problems. 

FinFET SRAM cells suffer from front-end wearout mechanisms, such as bias temperature instability and hot carrier injection. In this paper, we built a library based on deep neural networks (DNNs) to speed up the process of simulating FinFET SRAM cells' degradation. This library consists of two parts. The first part calculates circuit configuration parameters, wearout parameters, and the other input variables for the DNN. The second part calls for the DNN to determine the shifted circuit performance metrics. A DNN with more than 99% accuracy is achieved with training data from standard Hspice simulations. The correctness of the DNN is also validated in the presence of input variations. With this library, the simulation speed is one hundred times faster than Hspice simulations. We can display the cell's degradation under various configurations easily and quickly. Also, the DNN-based library can help protect intellectual property without showing users the circuit's details. 

We build a modelling and simulation flow to study how the front-end wearout mechanisms affect the FinFET SRAM soft error rate. This flow incorporates process variation, such as device dimensions, and degradation parameters. We first checked the impact of process parameters on critical charge and soft error rate. It is found that a larger gate length and higher temperature help us obtain better reliability for a FinFET SRAM cell under radiation, with a higher Qcrit and lower SER. Then, the time-dependent shift of Qcrit and SER is displayed. Within its range between 0% and 50%, a lower duty ratio leads to worse reliability due to soft errors. Moreover, a higher transition rate causes worse reliability.