A Novel 8T TFET-MOSFET Hybrid SRAM Cell for Ultra-Low Power and Computing In-Memory Applications

In this work, a novel 8T Tunnel FET (TFET)-MOSFET hybrid SRAM design with awareness of the impacts of large gate-drain capacitance in TFET is proposed and simulated. The proposed design utilizes MOSFET and TFET as upper and down transistor respectively in the read path, which can avoid the voltage coupling in conventional cascade TFET structure. Compared with conventional 6T CMOS SRAM, it achieves significant reduction in static and operation power, as well as improved static noise margin (SNM), leading to the lowest read power-delay-product (PDP). Moreover, its application for multiply-and-accumulate (MAC) operations in analog computing in-memory (CIM) is also demonstrated with high energy efficiency showing its great potential for energy-efficient AIoT applications.

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Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1109/cstic64481.2025.11017934
Citations
2
Primary Topic
Advancements in Semiconductor Devices and Circuit Design
Type
article
Field-Weighted Citation Impact
1.46
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article

A Novel 8T TFET-MOSFET Hybrid SRAM Cell for Ultra-Low Power and Computing In-Memory Applications

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2 citations
Advancements in Semiconductor Devices and Circuit Design
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article

A Novel 8T TFET-MOSFET Hybrid SRAM Cell for Ultra-Low Power and Computing In-Memory Applications

Yi Zhou, Qianqian Huang, Ru Huang, Kaifeng Wang, Rong Zhao, Rundong Jia
article en
2 citations

Abstract

In this work, a novel 8T Tunnel FET (TFET)-MOSFET hybrid SRAM design with awareness of the impacts of large gate-drain capacitance in TFET is proposed and simulated. The proposed design utilizes MOSFET and TFET as upper and down transistor respectively in the read path, which can avoid the voltage coupling in conventional cascade TFET structure. Compared with conventional 6T CMOS SRAM, it achieves significant reduction in static and operation power, as well as improved static noise margin (SNM), leading to the lowest read power-delay-product (PDP). Moreover, its application for multiply-and-accumulate (MAC) operations in analog computing in-memory (CIM) is also demonstrated with high energy efficiency showing its great potential for energy-efficient AIoT applications.

Anhui University (CN), Peking University (CN)
Affordable and clean energy
Openalex Percentile: Top 17%
Advancements in Semiconductor Devices and Circuit Design
1.46
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