Independent back bias in the ultrathin body and Box (UTBB) fully depleted silicon-on-insulator (FDSOI) serves as the critical knob for exploiting the performance and power tradeoffs and process/aging compensation. The effectiveness of back bias is one of the major metrics in the stages of circuit design and runtime feedback. In this article, new closed-form expressions accounting for the interface traps and short channel effects-dependent back gate capability in the UTBB FDSOI device are proposed. The developed model allows the accurate assessment of body factor ( $\gamma $ ) with the dependence of front/back interface trap density in the ultrascaled FDSOI device, taking the impact of gate area scaling as well as quantum effects into account. It also facilitates the evaluation of back bias effectiveness variability induced by interface traps and benefits the dynamic threshold voltage strategy in terms of adaptive back-biasing for performance optimization, which is consistent with 3-D device simulations and measurements. Moreover, the results confirm that $\gamma $ variability follows Pelgrom’s rule.

中文翻译：

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超薄体和盒式 FDSOI MOSFET 中与界面陷阱相关的反向偏置能力和可变性的分析模型

超薄机身和 Box (UTBB) 完全耗尽绝缘体上硅 (FDSOI) 中的独立反向偏置是利用性能和功率权衡以及工艺/老化补偿的关键旋钮。反向偏置的有效性是电路设计和运行时反馈阶段的主要指标之一。在本文中，提出了新的封闭形式表达式，用于解释 UTBB FDSOI 器件中的界面陷阱和依赖于短沟道效应的背栅能力。开发的模型允许准确评估身体因素（ $\伽马$ ) 与超尺度 FDSOI 器件中的前/背界面陷阱密度相关，同时考虑了栅极面积缩放和量子效应的影响。它还有助于评估由界面陷阱引起的反向偏置有效性可变性，并有利于动态阈值电压策略在性能优化的自适应反向偏置方面，这与 3-D 器件模拟和测量一致。此外，结果证实， $\伽马$ 可变性遵循 Pelgrom 规则。