JOURNAL ARTICLE
A FinFET-Based Low Leakage 10T Static Random Access Memory Cell.
Published In: Journal of Circuits, Systems & Computers, 2025, v. 34, n. 4. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Srivastav, Anandita; Tiwari, Usha; Mandal, Sushanta K.; Sachdeva, Ashish 3 of 3
Abstract
In this work, a transmission gate-based low-power ten FinFET (TGLP10T) single-ended read/write SRAM cell is proposed. The proposed cell achieves low leakage operation along with improved write-ability. The performance metric results obtained from TGLP10T are compared with five previously published bit-cell designs, i.e., conventional 6T (Con6T), robust transmission gate-based 10T SRAM (RTG10T), transmission gate-based variation resistant 9T SRAM (TGVR9T), PNN (PMOS-NMOS-NMOS) inverter-based 10T (PNN10T) and PPN (PMOS-PMOS-NMOS) inverter-based read decoupled 10T (PPNRD10T. The leakage power in proposed design is reduced by 1. 2 7 × ∕ 1. 1 5 × ∕ 1. 3 3 × ∕ 1. 1 5 × ∕ 1. 0 0 × compared to Con6T/RTG10T/TGVR9T/PNN10T/PPNRD10T cells. The write power and WSNM are improved by 1. 0 7 × ∕ 1. 4 0 × ∕ 0. 8 7 × ∕ 1. 6 3 × ∕ 0. 7 8 × and 1. 5 7 × ∕ 1. 6 9 × ∕ 1. 0 8 × ∕ 1. 5 9 × ∕ 1. 5 0 × ∕ 1. 0 0 × , respectively, compared to Con6T/RTG10T/TGVR9T/PNN10T/PPNRD10T cells. The read delay and write delay are improved by 1. 0 0 × ∕ 1. 5 4 × ∕ 1. 3 6 × ∕ 1 × ∕ 2. 8 3 × and 1.68 × ∕ 2. 3 5 × ∕ 1. 8 2 × ∕ 2. 8 9 × ∕ 1. 7 7 × , respectively, compared to Con6T/RTG10T/TGVR9T/PNN10T/PPNRD10T cells. The proposed design shows the least data retention voltage compared to other SRAM bit-cells. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Circuits, Systems & Computers. 2025/03, Vol. 34, Issue 4, p1
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:0218-1266
- DOI:10.1142/S0218126625500975
- Accession Number:183810552
- Copyright Statement:Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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