A 0.4 V, 12.2 pW Leakage, 36.5 fJ/Step Switching Efficiency Data Retention Flip-Flop in 22 nm FDSOI

Data-retention flip-flops (DR-FFs) efficiently maintain data during sleep mode, and retain state during transitions between active and sleep mode. This brief proposes an ultralow power DR-FF design with an improved autonomous data-retention (ADR) latch operating with a supply voltage range down to near/subthreshold, achieving a sleep mode leakage power of 12.2 pW, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.8\times $ </tex-math></inline-formula> less than the prior CMOS DR-FFs. Our proposed DR-FFs consume the lowest active mode switching efficiency of 36.5 fJ/step, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> less than the prior works, and a comparable transition efficiency of 1.9 fJ/step. Furthermore, our proposed DR-FFs require minimal control signals, logic gates, and switches, significantly reducing design complexity, and avoiding the drawbacks of nonvolatile data retention FFs (NV-FFs).

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Publication Details

Journal
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Published
2024-09-20
DOI
https://doi.org/10.1109/tvlsi.2024.3453946
Citations
2
Primary Topic
Neuroscience and Neural Engineering
Type
article
Field-Weighted Citation Impact
0.54

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article

A 0.4 V, 12.2 pW Leakage, 36.5 fJ/Step Switching Efficiency Data Retention Flip-Flop in 22 nm FDSOI

Fakhrul Zaman Rokhani, Changyan Chen, Yehea Ismail, Yuhang Zhang et al.
2 citations
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Neuroscience and Neural Engineering
0.54
article

A 0.4 V, 12.2 pW Leakage, 36.5 fJ/Step Switching Efficiency Data Retention Flip-Flop in 22 nm FDSOI

Fakhrul Zaman Rokhani, Changyan Chen, Yehea Ismail, Yuhang Zhang, Yuxin Ji, Yongfu Li, Jian Zhao
article en
2 citations

Abstract

Data-retention flip-flops (DR-FFs) efficiently maintain data during sleep mode, and retain state during transitions between active and sleep mode. This brief proposes an ultralow power DR-FF design with an improved autonomous data-retention (ADR) latch operating with a supply voltage range down to near/subthreshold, achieving a sleep mode leakage power of 12.2 pW, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.8\times $ </tex-math></inline-formula> less than the prior CMOS DR-FFs. Our proposed DR-FFs consume the lowest active mode switching efficiency of 36.5 fJ/step, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times $ </tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> less than the prior works, and a comparable transition efficiency of 1.9 fJ/step. Furthermore, our proposed DR-FFs require minimal control signals, logic gates, and switches, significantly reducing design complexity, and avoiding the drawbacks of nonvolatile data retention FFs (NV-FFs).

IEEE Transactions on Very Large Scale Integration (VLSI) SystemsVol. 33(2)
Universiti Putra Malaysia (MY), Shanghai Jiao Tong University (CN), American University in Cairo (EG)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 35%
Neuroscience and Neural Engineering
0.54
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