NBTI in Nanoscale MOSFETs—The Ultimate Modeling Benchmark
NBTI in Nanoscale MOSFETs—The Ultimate Modeling Benchmark
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DOI：
10.1109/ted.2014.2353578
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发表时间：
2014-09
影响因子：
3.1
通讯作者：
T. Grasser;K. Rott;H. Reisinger;M. Waltl;F. Schanovsky;B. Kaczer
中科院分区：
文献类型：
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作者：
T. Grasser;K. Rott;H. Reisinger;M. Waltl;F. Schanovsky;B. Kaczer
经过近半个世纪对偏置温度不稳定性的研究，两类模型成为最有力的竞争者。一类模型，反应扩散模型，是建立在氢从界面释放的想法，它是某种形式的氢的扩散，控制降解和恢复。虽然反应扩散理论的各种变体在过去的几年中已经被发表，但最常用的模型是基于非色散反应速率和非色散扩散。另一类模型是基于这样的想法，即降解是由广泛分布（分散）反应速率的一级反应控制的。我们证明，这两个类给出了根本不同的预测的随机降解和纳米器件的恢复，因此提供了最终的建模基准。使用详细的实验时间相关的缺陷光谱数据上获得的纳米器件，我们调查这些模型与实验的兼容性。我们的研究结果表明，氢（或任何其他物种）的扩散不太可能是决定降解的限制方面。另一方面，数据与反应限制模型完全一致。最后，我们认为，只有正确理解的物理机制，导致显着的设备到设备的变化，观察到的纳米器件的退化将使准确的可靠性预测和设备优化。
After nearly half a century of research into the bias temperature instability, two classes of models have emerged as the strongest contenders. One class of models, the reaction-diffusion models, is built around the idea that hydrogen is released from the interface and that it is the diffusion of some form of hydrogen that controls both degradation and recovery. Although various variants of the reaction-diffusion idea have been published over the years, the most commonly used recent models are based on nondispersive reaction rates and nondispersive diffusion. The other class of models is based on the idea that degradation is controlled by first-order reactions with widely distributed (dispersive) reaction rates. We demonstrate that these two classes give fundamentally different predictions for the stochastic degradation and recovery of nanoscale devices, therefore providing the ultimate modeling benchmark. Using detailed experimental time-dependent defect spectroscopy data obtained on such nanoscale devices, we investigate the compatibility of these models with experiment. Our results show that the diffusion of hydrogen (or any other species) is unlikely to be the limiting aspect that determines degradation. On the other hand, the data are fully consistent with reaction-limited models. We finally argue that only the correct understanding of the physical mechanisms leading to the significant device-to-device variation observed in the degradation in nanoscale devices will enable accurate reliability projections and device optimization.