FeRAM Capacitor with Novel Low-Power, Non-destructive and High Endurance Read operation for High-density Embedded Memory
For the first time, advanced CMOS compatible HZO-based FeRAM trench capacitors, are demonstrated with non-destructive read (R) endurance <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$> 10^{4}$</tex> cycles at elevated temperature (85°C) after single write (W). This ensures continuous R between refresh cycles and eliminates rewrite after every R, leading to further reduction in both latency and energy of R operation, over FeRAM with destructive R. Compared to published non-destructive read operation (NDRO) scheme, a higher R bias of 0.8V is used to obtain <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$> 28\times$</tex> memory window (MW) boost [1], [2], which originates from high-charge response from domain walls (DWs) due to partial polarization switching. Furthermore, 5yr lifetime at elevated temperature is extrapolated under R/W-intensive NDRO, while low-frequency W access (or refresh) is identified as the worst-case scenario. Finally, a tight distribution of functional bits with NDRO in FeRAM capacitor array across 300mm wafer is demonstrated, showing that integrating low-voltage FE capacitors with proposed novel NDRO scheme has great performance potential for next-generation low-power, high-speed, dense embedded memory.
Authors
- Uygar E. Avci (ORCID: https://orcid.org/0000-0002-0109-1923)
- B. Krist
- Gyu-Yeong Choe
- C. Neumann
- J. Kavalieros
- Bernal Granados Alpizar
- Shahbaz Ahmed Siddiqui (ORCID: https://orcid.org/0000-0002-2256-3897)
- Sou-Chi Chang (ORCID: https://orcid.org/0000-0001-8128-7784)
- Chih‐Chien Lin (ORCID: https://orcid.org/0000-0001-9585-8234)
- M. Metz
- B. Bangalore Rajeeva
- A. Augustus
Institutions
- Intel (United States) (US)
Publication Details
- Published
- 2025-06-08
- DOI
- https://doi.org/10.23919/vlsitechnologyandcir65189.2025.11075099
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19