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通过高通量设计在铁电道交叉点中的巨型电阻:朝着高性能神经形态计算

Hong Fang^1,2, Jie Wang^1,2, Fang Nie³

¹Functional Materials and Acousto-Optic Instruments Institute, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China.

ACS applied materials & interfaces

|December 29, 2023

概括

研究人员为铁电道交叉点 (FTJ) 开发了一种新的道屏障装饰策略,实现了创纪录的ON/OFF比率10^8.8. 这一突破增强了数据存储和神经形态计算潜力.

科学领域:

材料科学材料科学材料科学
凝聚物质物理学凝聚物质物理学
纳米技术纳米技术

背景情况:

铁电道连接 (FTJ) 是下一代数据存储和神经形态计算的前景,因为它们的速度,低功耗和可堆叠性.
提高FTJ性能的传统工程方法存在局限性.
提高ON/OFF比率对于实际的FTJ应用至关重要.

研究的目的:

制定一项新的道屏障装饰策略,以显著提高铁电道结口 (FTJ) 的启/关比.
调查超薄介电层插入对铁电性质和道行为的影响.
展示工程FTJ在人工神经网络中用于神经形态计算的应用.

主要方法:

采用了道屏障装饰策略,使用高通量技术定期将超薄的SrTiO3 (STO) 介电层安装在BaTiO3 (BTO) 铁电层上.
研究了STO插入对BTO层局部四边形性和铁电性的影响.
优化氧气迁移以进一步操纵道障碍特征.

主要成果:

通过增强OFF状态和减少ON状态,实现了约10^8的创纪录的ON/OFF比率.
证明插入的STO层在BTO道层中增强了铁电.
使用基于FTJ的人工突触模拟了一个人工神经网络,达到92%的分类准确度.

结论:

道屏障装饰策略,特别是原子层层插入,在FTJ设计中提供了显著的进步.
这种方法提供了对FTJs中道屏障行为的多重机制的基本理解.
高的ON/OFF比率和成功的神经网络模拟突显了这些工程FTJ在先进计算应用中的潜力.

关键词:

人工神经突触可以通过人工神经突触.铁电道的结点铁电道的结点高通量技术的高通量技术电阻开关电阻开关道挖掘电阻电阻的道

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