高可靠性和自我校正的离子记忆器通过底部电极设计和剂结合
Byeong Min Lim1,2, Yu Min Lee1,2, Chan Sik Yoo1,2
1Department of Advanced Materials Engineering for Information and Electronics, Kyung Hee University, Yongin 17104, Republic of Korea.
ACS nano
|February 13, 2024
概括
这项研究开发了一种改进的用于人工神经网络的离子记忆器. 新的设计提高了可靠性和性能,这对于神经形态硬件应用至关重要.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机科学 计算机科学
背景情况:
- 离子记忆器是神经形态硬件中人工神经网络 (ANN) 计算的关键.
- 交叉阵列架构 (CAA) 的挑战包括设备可靠性和潜入电流.
- 接口类型的离子记忆器提供了优势,但与缓慢的重量更新和不良的保留/耐久性作斗争.
研究的目的:
- 开发一种离子自我纠正的memristor,提高了神经形态硬件的可靠性和性能.
- 为了解决现有的离子记忆器中缓慢的重量更新,不良的保留和耐久性的局限性.
主要方法:
- 使用底部电极设计与 (Li) 金属粘附层制造离子自我纠正的memristor.
- 在Na:TiO2的原子层沉积过程中,通过扩散形成离子储库.
- 加入 (Al) 剂以抑制酸盐扩散并改善保留.
主要成果:
- 优化Al-doped记忆器在125°C保持时间>20小时,耐力>5.5×105周期.
- 在重量更新特征中显示出高线性和对称性.
- 在32 × 32 CAA上的可靠性测试显示,设备对设备 (81%) 和循环对循环 (8%) 的变化很低.
结论:
- 开发的离子记忆器有效地克服了以前设计的局限性.
- 新型电极设计和Al doping显著提高了用于神经形态应用的memristor性能和可靠性.
- 这一进步对于实现高效和强大的人工神经网络硬件至关重要.
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