用于内存计算的反铁磁隧道结：建模与案例研究
Antiferromagnetic Tunnel Junctions (AFMTJs) for In-Memory Computing: Modeling and Case Study
AI总结：
AFMTJs通过多子晶格LLG动力学与电路建模结合，实现内存计算中的低功耗高效运算，显著优于传统MTJ方案。
摘要：
反铁磁隧道结（AFMTJs）通过超快子晶格动力学实现皮秒级切换和飞焦耳级写入。我们提出了首个端到端的AFMTJ模拟框架，整合了多子晶格Landau-Lifshitz-Gilbert（LLG）动力学与电路级建模。基于SPICE的模拟显示，AFMTJs的写入延迟和写入能量分别比传统MTJs低约8倍和9倍。当集成到内存计算架构中时，AFMTJs相比CPU基线实现了平均17.5倍的速度提升和几乎20倍的能耗节省，显著超越基于MTJ的内存计算。这些结果确立了AFMTJs作为可扩展、低功耗计算的有吸引力的基本元件。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
