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).
Authors
- Fakhrul Zaman Rokhani (ORCID: https://orcid.org/0000-0001-6449-8184)
- Changyan Chen (ORCID: https://orcid.org/0000-0002-4608-1948)
- Yehea Ismail (ORCID: https://orcid.org/0000-0003-3956-7533)
- Yuhang Zhang (ORCID: https://orcid.org/0000-0002-4101-6207)
- Yuxin Ji (ORCID: https://orcid.org/0000-0001-8660-4929)
- Yongfu Li (ORCID: https://orcid.org/0000-0002-6322-8614)
- Jian Zhao (ORCID: https://orcid.org/0000-0002-6741-6647)
Institutions
- Universiti Putra Malaysia (MY)
- Shanghai Jiao Tong University (CN)
- American University in Cairo (EG)
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
Funders
- National Natural Science Foundation of China