Current source based cell models are becoming a necessity for accurate timing and noise analysis at 65nm and below. Voltage waveform shapes are increasingly more difficult to represent as simple ramps due to highly resistive interconnects and Miller cap effects at receiver gates. Propagation of complex voltage waveforms, and accurate modeling of nonlinear driver and receiver effects in crosstalk noise analysis require accurate cell models. A good cell model should be independent of input waveform and output load, should be easy to characterize and should not increase the complexity of a cell library with high-dimensional look-up tables. At the same time, it should provide high accuracy compared to SPICE for all analysis scenarios including multiple-input switching, and for all cell types and cell arcs, including those with high stacks. It should also be easily extendable for use in statistical STA and noise analysis, and one should be able to simulate it fast enough for practical use in multi-million gate designs. In this paper, we present a gate model built from fast transistor models (FXM) that has all the desired properties. Along with this model, we also present a multithreaded timing traversal approach that allows one to take advantage of the high accuracy provided by the FXM, at traditional STA speeds. Results are presented using a fully extracted 65nm TSMC technology.
The trustworthiness of the mobile nodes is considered as the predominant parameter for ensuring significant data dissemination in the ad hoc network. However, the selfishness activity of the mobile nodes minimizes the trust of the mobile nodes by dropping a considerable number of data
packets in the network. The significant dropping of data packets by the selfish node introduces huge data overhead with increased latency and energy consumptions by increasing the number of retransmissions. In this paper, a Bates Distribution Inspired Trust Factor-based Selfish Node Detection
Technique (BDITF-SNDT) is proposed for predominant detection of selfish behavior by investigating multiple levels of factors that contribute towards effective selfishness detection. This proposed BDITF-SNDT approach is also potent in enhancing the detection rate of selfishness through the
multi-perspective analysis of each monitored mobile nodes' forwarding characteristics towards the benefits of the other interacting mobile nodes. The simulation experiments and results of the proposed BDITF-SNDT approach is determined to be enhanced on an average by 16% and 14% superior to
the compared selfish node isolation approaches existing in the literature.
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