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Variability Aware Simulation Based Design- Technology Cooptimization (DTCO) Flow in 14 nm FinFET/SRAM Cooptimization

机译:14 nm FinFET / SRAM协同优化中基于变量感知仿真的设计技术协同优化(DTCO)流程

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In this paper, we use an automated tool flow in a 14 nm CMOS fin-shaped field-effect transistor (FinFET)/ static random access memory (SRAM) simulation-based design-technology cooptimization (DTCO) including both process-induced and intrinsic statistical variabilities. A 22 nm FinFET CMOS technology is used to illustrate the sensitivity to process-induced fin shape variation and to motivate this paper. Predictive Technology Computer Aided Design (TCAD) simulations have been carried out to evaluate the transistor performance ahead of silicon. Draft-diffusion simulations calibrated to the ensemble Monte Carlo simulation results are used to explore the process and the statistical variability space. This has been enabled by the automation of the tool flow and the dataset handling. The interplay between the process and the statistical variability has been examined in details. A two-stage compact model strategy is used to capture the interplay between process and statistical variability. To close the DTCO loop, the static noise margin and write noise margin sensitivity to cell design parameters and variability in FinFET-based SRAM designs are studied in details.
机译:在本文中,我们在14 nm CMOS鳍状场效应晶体管(FinFET)/静态随机存取存储器(SRAM)基于仿真的设计技术协同优化(DTCO)中使用了自动化的工具流程,该过程包括过程诱导的和固有的统计差异。 22 nm FinFET CMOS技术用于说明对工艺引起的鳍形变化的敏感性并激发本文的兴趣。已经进行了预测技术计算机辅助设计(TCAD)仿真,以评估领先于硅的晶体管性能。校准到整体蒙特卡洛模拟结果的吃水扩散模拟用于探索过程和统计变异性空间。这是通过自动化工具流程和数据集处理实现的。详细检查了过程与统计变异性之间的相互作用。两阶段紧凑模型策略用于捕获过程和统计变异性之间的相互作用。为了闭合DTCO环路,详细研究了静态噪声容限和写噪声容限对单元设计参数的敏感性以及基于FinFET的SRAM设计中的可变性。

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