基于udsm的晶体管堆全静态泄漏估计新模型

H. Al-Hertani, D. Al-Khalili, C. Rozon
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引用次数: 1

摘要

介绍了一种新的基于输入模式的超深亚微米工艺总静态泄漏估计模型。该模型将闸门隧道泄漏、闸门诱发漏漏和亚阈值泄漏集成到一个单一的泄漏估计框架中。亚阈值估计是通过分析估计OFF晶体管之间的节点电压来实现的,而栅极隧穿漏和GIDL则是基于各自BSIM4方程的简化版本来计算的。该框架处理所有输入模式,并适应各种泄漏电流相互作用的场景。文献中类似的方法要么基于查找表方法,不适应不同宽度的晶体管堆栈,要么是高度实验性的,需要对晶体管器件物理有详细的了解。目前也有几种方法分别对阈下泄漏和隧洞泄漏进行建模。即使这些方法使用查找表方法,固定晶体管堆栈中的所有宽度和/或限制堆栈大小为2-3个晶体管。本文提出的模型易于处理,几乎完全是分析性的。它能够容纳多达4个晶体管的堆栈,并具有不同的晶体管宽度。基于该模型在MatLab中编写了适用于65nm、45nm和32nm PTM工艺的堆栈估计函数。与SPICE模拟相比,该模型在堆栈大小为1、2、3和4时的平均误差分别为1.29%、2.79%、7.57%和11.42%。与SPICE相比,该模型还显示出显著的运行时节省。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new total static leakage estimation model for UDSM-based transistor stacks
This paper introduces a new input pattern dependent model for total static leakage estimation in ultra deep submicron processes. The model integrates gate tunnelling leakage, gate induced drain leakage (GIDL) and subthreshold leakage into a single leakage estimation framework. Subthreshold estimation is facilitated through the analytical estimation of nodal voltages between OFF transistors, while gate tunnelling leakage and GIDL are calculated based on simplified versions of their respective BSIM4 equations. The framework deals with all input patterns and accommodates scenarios where the various leakage currents interact. Similar approaches in the literature are either based on a look up table approach, and do not accommodate transistor stacks with varying widths, or are highly experimental and require a detailed knowledge of the transistor device physics. Several approaches also exist for modeling either subthreshold leakage or gate tunnelling leakage separately. Even those approaches use a lookup table approach, fix all widths in a transistor stack and/or limit the stack size to 2-3 transistors. The model proposed in this paper is tractable and almost completely analytical. It is capable of accommodating stacks with up to 4 transistors with varying transistor widths. A stack estimator function based on this model was coded in MatLab for the 65nm, 45nm and 32nm PTM process technologies. Compared with SPICE simulations the model exhibited an average error of 1.29%, 2.79%, 7.57% and 11.42% for stack sizes of 1, 2, 3 and 4 respectively across all three technologies. The model also exhibits significant runtime savings when compared with SPICE.
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