Ternary Content Addressable Memory With Ferroelectric Non-Volatile Capacitor
In this letter, a ferroelectric field effect transistor (FeFET)-based charge-domain ternary content addressable memory (TCAM) is experimentally demonstrated for in-memory search. Specifically, foundry based FeFETs are configured in non-volatile capacitor (nvCAP) mode for charge-domain computation. A single TCAM cell consisting of 2 nvCAPs (i.e. two FeFETs) is used to perform the Store: bit ‘0’, ‘1’, ‘X’ and Search: bit ‘0’, ‘1’, ‘X’ operations. Further, SPICE simulations are performed to evaluate the feasibility of a scaled nvCAP device in a large TCAM array. The low C<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}_{\mathbf {\textit {ON}}}$ </tex-math></inline-formula>/C<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}_{\mathbf {\textit {OFF}}}$ </tex-math></inline-formula> ratio of a scaled device can lead to false-positive matchings, which can be countered by introducing a dummy column and input dependent charge cancellation. Finally, the charge-domain nvCAP TCAM is benchmarked against other proposed candidates, showing a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 1.5\times $ </tex-math></inline-formula> improvement in energy efficiency over FeFET based current domain TCAM, and a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 3.3\times $ </tex-math></inline-formula> improvement compared to resistive random access memory (RRAM) based TCAM.
Authors
- Halid Mulaosmanovic (ORCID: https://orcid.org/0000-0001-9524-5112)
- Omkar Phadke (ORCID: https://orcid.org/0000-0001-5606-6242)
- James Read (ORCID: https://orcid.org/0000-0003-0753-6257)
- Po-Kai Hsu (ORCID: https://orcid.org/0000-0002-7518-9472)
- Junmo Lee (ORCID: https://orcid.org/0000-0001-6092-6436)
- Shimeng Yu (ORCID: https://orcid.org/0000-0002-0068-3652)
- Yuan-Chun Luo (ORCID: https://orcid.org/0000-0001-5793-075X)
- Stefan Dünkel
- Janak Sharda (ORCID: https://orcid.org/0000-0002-1438-2439)
- V. Garg (ORCID: https://orcid.org/0009-0008-7589-2943)
- Sven Beyer
Institutions
- Georgia Institute of Technology (US)
- GlobalFoundries (Germany) (DE)
Publication Details
- Journal
- IEEE Electron Device Letters
- Published
- 2025-03-22
- DOI
- https://doi.org/10.1109/led.2025.3553473
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- Defense Advanced Research Projects Agency