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

Abstract

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.

Keywords

• BiFeO₃ (BFO)
• magnetoelectric (ME)
• ME-magnetic random access memory (MRAM)
• memory-augmented neural network (MANN)
• micromagnetic
• multiferroic