Modeling and Design for Magnetoelectric Ternary Content Addressable Memory (TCAM)

This article proposes a novel magnetoelectric (ME) effect-based ternary content addressable memory (TCAM). The potential array-level write and search performances of the proposed ME-TCAM are studied using experimentally calibrated compact physical models and SPICE simulations. The voltage-controlled operation of the ME devices eliminates the large joule heating present in the current-controlled magnetic devices and their low-voltage write operation makes them more energy-efficient compared to static random access memory-based TCAMs (SRAM-TCAMs). The proposed compact TCAM outperforms its SRAM counterpart with
$1.35\times $
and
$14.4\times $
improvements in search and write energy, respectively, and its nonvolatility eliminates the standby leakage. We project an error rate below
$10^{-4}$
while considering various sources of variation in magnetic and CMOS devices. At the application level, using memory-augmented neural networks (MANNs), we project a
$2\times $
energy-delay–area-product (EDAP) improvement over an SRAM-TCAM.