在HfZrO2-基于HfZrO2的高性能突触阵列的道连接处调节开关机制
Jiwon You1, Jeong-Han Kim2, Minsuk Song3
1Department of AI Semiconductor Engineering, Hanyang University, Seoul, Republic of Korea.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|February 23, 2026
概括
有混合开关的优化铁电道连接 (FTJ) 阵列表现出高道电阻 (TER) 以实现高效的神经形态计算. 这些可扩展的数组使先进的非挥发性内存和节能AI硬件成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 固态物理 固态物理
- 设备工程 设备工程
背景情况:
- 铁电道连接 (FTJ) 是下一代非挥发性内存和神经形态计算的关键组件.
- 将FTJ可扩展集成到大型阵列中对于实际应用至关重要.
- 控制FTJ中的开关机制对于优化设备性能至关重要.
研究的目的:
- 研究可扩展的混合开关FTJ阵列的材料和结构工程.
- 系统地操纵HfZrO2 (HZO) 薄膜中的氧空隙度.
- 为了实现不同的操作模式并提高道电阻 (TER) 性能.
主要方法:
- 底部电极 (Mo,Mo/Ti) 和接口层 (ZrO2) 的战略选择.
- 在HfZrO2膜中控制氧空位 (VO) 度的操纵.
- 42x42 FTJ阵列的制造和集成到视觉变压器 (ViT) 架构中.
主要成果:
- 实现了三种操作模式:纯铁电,缺陷调制和混合开关.
- 证明了高TER比率:~10^2与Mo,>10^4与Mo/Ti底部电极.
- 制造的FTJ阵列在ViT中显示了统一的多层电导度调制和稳定的VMM操作.
结论:
- 精确设计的混合开关FTJ阵列为先进的内存和神经形态系统提供了可扩展和节能的平台.
- 材料和结构的优化对于实现高性能FTJ设备至关重要.
- 集成的FTJ阵列可以实现稳定和高效的AI计算,克服设备可变性挑战.
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