10nm以下的HfZrO铁电突触具有多层和不同比率用于神经形态计算
Bo Chen1, Chengcheng Wang1, Xuepeng Zhan1
1School of Information Science and Engineering (ISE), Shandong University, Qingdao, People's Republic of China.
Nanotechnology
|September 19, 2023
概括
研究人员使用不同的HfZrO组件开发了10nm以下的铁电容器,用于节能的神经形态计算. 这些人造突触显示出对超大规模设备在MNIST识别等任务中的承诺.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机工程 计算机工程
- 电气工程 电气工程
背景情况:
- 新兴的非易失性记忆对于克服神经形态计算硬件中的诺伊曼瓶至关重要.
- 设备缩放和低操作电压是高集成和高能效神经形态系统的关键.
研究的目的:
- 为神经形态应用制造和表征10nm以下的铁电电容器,具有不同的HfZrO组件.
- 研究组件变化的对铁电性质和突触模拟能力的影响.
主要方法:
- 使用具有不同HfO和ZrO组成的HfZrO (HZO) 薄膜制造10nm以下的铁电容器.
- 铁电性质的表征,包括残余极化和强制电场 (Ec).
- 在MNIST手写数字识别任务中评估人工突触性能.
主要成果:
- 与传统 1:1 HZO 电容器相比,不同组件的 HZO 电容器表现出类似的残余极化,但强制电场 (Ec) 较低.
- 低Ec能够在较低的脉冲幅度和宽度进行部分域切换,这对于突触模拟至关重要.
- 在10nm以下的铁电人工突触在MNIST识别任务中实现了大约85.83%的准确性.
结论:
- 不同组件的铁电HZO电容器为超大规模的神经形态计算提供了优势.
- 这些设备显示出开发下一代节能的人工突触的潜力.
- 这些发现为使用超大规模铁电装置的先进神经形态系统铺平了道路.
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