2nm Node: Benchmarking FinFET vs Nano-Slab Transistor Architectures for Artificial Intelligence and Next Gen Smart Mobile Devices

S. Song, B. Colombeau, M. Bauer, V. Moroz, X. Lin, P. Asenov, D. Sherlekar, M. Choi, J. Huang, B. Cheng, C. Chidambaram, S. Natarajan
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引用次数: 9

Abstract

We explore four different technology and design options for transistors and library cells for a low power supply voltage of 0.4 V and circuit statistics representative of artificial intelligence (AI) applications. The design rules correspond to 2nm node with cell heights of 100~110 nm and 30 nm gate pitch. Holistic analysis of the RO (Ring Oscillator) behavior, including MOL parasitics, all major variability sources, and stress proximity effects suggests that different FinFET and nanoslab transistor design options exhibit a wide range of power and performance differences. The key to improve FinFET PPA is to avoid fin cuts to maintain strong PMOS performance, and a key to improve SS corner delay is to use nanoslabs with tighter variability control.
2nm节点:用于人工智能和下一代智能移动设备的FinFET与纳米板晶体管架构的基准
我们探索了四种不同的技术和设计方案,用于0.4 V的低电源电压的晶体管和库单元,以及代表人工智能(AI)应用的电路统计。设计规则对应于2nm节点,单元高度为100~110 nm,栅极间距为30 nm。对环形振荡器(RO)行为的整体分析,包括MOL寄生、所有主要变异性源和应力接近效应,表明不同的FinFET和纳米板晶体管设计选项显示出广泛的功率和性能差异。改善FinFET PPA的关键是避免翅片切割以保持较强的PMOS性能,改善SS角延迟的关键是使用具有更严格可变性控制的纳米板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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