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arXiv · 2602.08323

Antiferromagnetic Tunnel Junctions (AFMTJs) for In-Memory Computing: Modeling and Case Study

Yousuf Choudhary·Tosiron Adegbija
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Abstract

Antiferromagnetic Tunnel Junctions (AFMTJs) enable picosecond switching and femtojoule writes through ultrafast sublattice dynamics. We present the first end-to-end AFMTJ simulation framework integrating multi-sublattice Landau-Lifshitz-Gilbert (LLG) dynamics with circuit-level modeling. SPICE-based simulations show that AFMTJs achieve ~8x lower write latency and ~9x lower write energy than conventional MTJs. When integrated into an in-memory computing architecture, AFMTJs deliver 17.5x average speedup and nearly 20x energy savings versus a CPU baseline-significantly outperforming MTJ-based IMC. These results establish AFMTJs as a compelling primitive for scalable, low-power computing.

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Yousuf Choudhary, Tosiron Adegbija. 2026-02-09. Antiferromagnetic Tunnel Junctions (AFMTJs) for In-Memory Computing: Modeling and Case Study. https://arxiv.org/abs/2602.08323

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