TY - GEN
T1 - Computational Associative Memory Powered by Ferroelectric Memory
AU - Ni, Kai
AU - Xiao, Yi
AU - Deng, Shan
AU - Narayanan, Vijaykrishnan
N1 - Funding Information:
Acknowledgement: This work is partially supported by SUPREME and PRISM centers, two of SRC/JUMP 2.0 centers. It is also partially supported by NSF under award #2239284. Reference: [1] K. Ni et al., Nature Electronics, vol. 2, p. 521, (2019). [2] X. Ma et al., IEEE Trans. VLSI Systems, vol. 30, p. 1770, (2022). [3] X. Yin et al., arXiv 2209.11971 (2022). [4] S. M. Yoon et al., IEEE Trans. Electron Devices, vol.48, p.2002, (2001). [5] S. Deng et al., VLSI Symp. (2020).
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.
AB - Discovery of ferroelectricity in thin doped Hf02 has revived the interest in ferroelectric memories because its excellent CMOS-compatibility, great scalability, and superior energy efficiency. Various types of ferroelectric memories are under consideration, including capacitor based ferroelectric random access memory (FeRAM) and transistor based ferroelectric field effect transistor (FeFET). Significant progress has been made by integrating them at advanced technology nodes. These exciting developments have made ferroelectric memories prime candidates as embedded nonvolatile memory and enable their application in compute-in-memory (CiM) accelerators. One important class of CiM kernel is content addressable memory (CAM), where the memory is addressed through its content. In this work, we overview different designs of FeFET based CAM and propose FeRAM based CAM.
UR - http://www.scopus.com/inward/record.url?scp=85167870033&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167870033&partnerID=8YFLogxK
U2 - 10.1109/DRC58590.2023.10187048
DO - 10.1109/DRC58590.2023.10187048
M3 - Conference contribution
AN - SCOPUS:85167870033
T3 - Device Research Conference - Conference Digest, DRC
BT - 2023 Device Research Conference, DRC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Device Research Conference, DRC 2023
Y2 - 25 June 2023 through 28 June 2023
ER -
