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Cited by 79Advanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesSemiconductor materials and devices

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Non-Volatile RRAM Embedded into 22FFL FinFET Technology

Golonzka, O., Arslan, U., Bai, P., Bohr, M., Baykan, O., Chang, Y., Chaudhari, A., Chen, A., Clarke, J., Connor, C., Das, N., English, C., Ghani, T., Hamzaoglu, F., Hentges, P., Jain, P., Jezewski, C., Karpov, I., Kothari, H., Kotlyar, R., Lin, B., Metz, M., Odonnell, J., Ouellette, D., Park, J., Pirkle, A., Quintero, P., Seghete, D., Sekhar, M., Gupta, A. Sen, Seth, M., Strutt, N., Wiegand, C., Yoo, H. J., Fischer, K.·2019·2019 Symposium on VLSI Technology
DOI 10.23919/vlsit.2019.8776570
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Abstract

This paper presents key specifications of RRAM-based nonvolatile memory embedded into Intel 22FFL FinFET Technology. 22FFL is a high performance, ultra low power technology developed for mobile and RF applications providing extensive high voltage and analog support and high design flexibility combined with low manufacturing costs [1]. Embedded RRAM technology presented in this paper achieves 104cycle endurance combined with 85°C 10-year retention and high die yield. Technology data retention, endurance and yield are demonstrated on 7.2Mbit arrays. We describe device characteristics, bit cell integration into the logic flow, as well as key considerations for achieving high endurance and retention properties.

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Research works printed but not linked: 6

  1. lee, 2017, A 1. 4Mb 40-nm embedded ReRAM macro with 0. 07um2 bit cell, 2. 7mA/100MHz low-power read and hybrid write verify for high endurance application, IEEE Asian Solid-State Circuits Conf, 9
  2. 10.1109/TED.2016.2639283 · DOI
  3. jain, 2019, A 3. 6Mb 10. 1Mb/mm2 embedded non-volatile ReRAM macro in 22nm FinFET technology with adaptive forming/set/reset schemes yielding down to 0. 5V with sensing time of 5ns at 0. 7V, ISSCC Tech Dig
  4. 10.1109/IEDM.2018.8614620 · DOI
  5. 10.1109/IMW.2018.8388846 · DOI
  6. 10.1109/IEDM.2017.8268475 · DOI

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Metadata

Authors
Golonzka, O., Arslan, U., Bai, P., Bohr, M., Baykan, O., Chang, Y., Chaudhari, A., Chen, A., Clarke, J., Connor, C., Das, N., English, C., Ghani, T., Hamzaoglu, F., Hentges, P., Jain, P., Jezewski, C., Karpov, I., Kothari, H., Kotlyar, R., Lin, B., Metz, M., Odonnell, J., Ouellette, D., Park, J., Pirkle, A., Quintero, P., Seghete, D., Sekhar, M., Gupta, A. S., Seth, M., Strutt, N., Wiegand, C., Yoo, H. J., & Fischer, K.
Affiliations
Intel (United States) (US)
ORCID
Venue
2019 Symposium on VLSI Technology
Publisher
IEEE
Published
2019-06
Pages
T230-T231
DOI
10.23919/vlsit.2019.8776570
OpenAlex
W2965096498
Cited by
79
References
0
Citations by year
2026: 6 · 2025: 10 · 2024: 13 · 2023: 11 · 2022: 16 · 2021: 11 · 2020: 11 · 2019: 1
FWCI
15.52
Type
conference paper
Language
en
Open access
closed
Locations
Symposium on VLSI Technology (publishedVersion)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Semiconductor materials and devices
Keywords
resistive random access memory, embedded non-volatile memory, memory endurance, data retention, die yield

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