使用LECTOR技术设计具有sgfinfet的高能效SRAM单元

Sivaiah Sankranti, S. Roji Marjorie
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引用次数: 0

摘要

静态随机存取存储器(SRAM)是数字系统的重要组成部分。SRAM单元的主要问题是功率泄漏,这会导致芯片面积的增加。因此,本文提出了一种利用泄漏控制晶体管技术(sgfinfet -SRAM- ector)的基于短栅翅片型场效应晶体管的SRAM单元,通过提高静态噪声裕度(SNMs)和功率延迟积(PDP)来降低泄漏功率延迟。在这里,sgfinfet - sram - lector主要用于堆叠增强,以减少泄漏功耗(LPD)。在LECTOR中增加了两个晶体管,通过晶体管堆叠来减小泄漏电流的延时。LECTOR采用两个以上的晶体管,在上拉和下拉网络之间串联连接,这意味着在上拉网络和输出端之间插入额外的SG finfet PMOS晶体管,在下拉网络和输出端插入额外的SG finfet NMOS晶体管。这些附加的晶体管可以减小漏电流。在HSPICE仿真工具中对该方法进行了仿真。计算了一些度量来验证所提出方法的有效性。最后,通过对现有模型的分析,该方法的读延迟降低11.31%、51.47%、45.46%,写延迟降低44.44%、26.33%、33.45%,读功率降低36.12%、45.28%、26.45%,写功率降低34.5%、33.56%、22.41%,高读SNM达到37.4%、15.3%、26.54%,高写SNM达到33.67%、35.8%、12.09%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing power-efficient SRAM cells with SGFinFETs using LECTOR technique
Static random-access memory (SRAM) plays a vital component of digital systems. The main issue of SRAM cells is power leakage, which results in an increase in chip area. Therefore this manuscript proposes a shorted-gate fin-type field-effect transistor based SRAM cell utilizing leakage control transistor technique (SGFinFETs-SRAM-LECTOR) for decreasing the leakage power delay by improving the static noise margins (SNMs) together with power delay product (PDP). Here, the SGFinFETs-SRAM-LECTOR is primarily applied to stacking enhancement for lessening the leakage power dissipation (LPD). Two more transistors are used in LECTOR for reducing the leakage current with delay, which is based on transistor stacking. LECTOR employs two more transistors that are connected in series between pull-up and pull-down networks that means additional SG FinFETs PMOS transistor insertions amongst the pull-up network and output terminal, additional SG FinFETs NMOS transistor insertions amidst the pull down network and output terminal. These additional transistors can decrease the leakage current. The simulation of the proposed approach is implemented in HSPICE simulation tool. Some metrics are computed to validate the efficacy of the proposed approach. Finally, the proposed technique reaches 11.31%, 51.47%, 45.46% less read delay, 44.44%, 26.33%, 33.45% less write delay, 36.12%, 45.28%, 26.45% less read power, 34.5%, 33.56%, 22.41% less write power, 37.4%, 15.3%, 26.54% high read SNM, 33.67%, 35.8%,12.09% high write SNM when analyzed to the existing models.
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