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A new physical and continuous BSIM (Berkeley Short-Channel IGFET Model) I-V model in BSIM3v3 is presented for circuit simulation. Including the major physical effects in state-of-the art MOS devices, the model describes current characteristics from subthreshold to strong inversion as well as from the linear to the saturation operating regions with a single I-V expression, and guarantees the continuities of Ids, conductances and their derivatives throughout all Vgs, Vds, and Tbs, bias conditions. Compared with the previous BSIM models, the improved model continuity enhances the convergence property of the circuit simulators. Furthermore, the model accuracy has also been enhanced by including the dependencies of geometry and bias of parasitic series resistances, narrow width, bulk charge, and DIBL effects. The new model has the extensive built-in dependencies of important dimensional and processing parameters (e.g., channel length, width, gate oxide thickness, junction depth, substrate doping concentration, etc.). It allows users to accurately describe the MOSFET characteristics over a wide range of channel lengths and widths for various technologies, and is attractive for statistical modeling. The model has been implemented in the circuit simulators such as Spectre, Hspice, SmartSpice, Spice3e2, and so on  相似文献
2.
The authors describe a novel self-converging programming method using the source-induced band-to-band hot electron (SIBE) injection. This method features low current, high speed, and good reliability, and automatically converges at the desired threshold voltage state without any conventional verification operations. The programming leakage current of this method is only about 3 /spl mu/A//spl mu/m, and the programming time is as low as 30 /spl mu/s. A threshold voltage model is also proposed and shows good consistency with measured results.  相似文献
3.
MapReduce has become a popular model for large‐scale data processing in recent years. Many works on MapReduce scheduling (e.g., load balancing and deadline‐aware scheduling) have emphasized the importance of predicting workload received by individual reducers. However, because the input characteristics and user‐specified map function of a given job are unknown to the MapReduce framework before the job starts, accurately predicting workload of reducers can be a difficult challenge. To address this challenge, we present ROUTE, a run‐time robust reducer workload estimation technique for MapReduce. ROUTE progressively samples the partition size of the early completed mappers, allowing ROUTE to perform estimation at run time yet fulfilling the accuracy requirement specified by users. Moreover, by using robust estimation and bootstrapping resampling techniques, ROUTE can achieve high applicability to a wide variety of applications. Through experiments using both real and synthetic data on an 11‐node Hadoop cluster, we show ROUTE can achieve high accuracy with error rate no more than 10.92% and an improvement of 40.6% in terms of error rate while compared with the state‐of‐the‐art solution. Besides, through simulations using synthetic data, we show that ROUTE is robust to a variety of skewed distributions. Finally, we apply ROUTE to existing load balancing and deadline‐aware scheduling frameworks and show ROUTE significantly improves the performance of these frameworks. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献
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