相变存储器
异质结
冯·诺依曼建筑
噪音(视频)
计算机科学
电阻随机存取存储器
重置(财务)
一致性(知识库)
计算机数据存储
记忆电阻器
材料科学
光电子学
电子工程
电气工程
计算机硬件
纳米技术
工程类
图层(电子)
人工智能
经济
电压
图像(数学)
操作系统
金融经济学
作者
Keyuan Ding,Jiangjing Wang,Yuxing Zhou,He Tian,Lu Lu,Riccardo Mazzarello,Chun‐Lin Jia,Wei Zhang,Feng Rao,E. Ma
出处
期刊:Science
[American Association for the Advancement of Science]
日期:2019-08-22
卷期号:366 (6462): 210-215
被引量:340
标识
DOI:10.1126/science.aay0291
摘要
Artificial intelligence and other data-intensive applications have escalated the demand for data storage and processing. New computing devices, such as phase-change random access memory (PCRAM)-based neuro-inspired devices, are promising options for breaking the von Neumann barrier by unifying storage with computing in memory cells. However, current PCRAM devices have considerable noise and drift in electrical resistance that erodes the precision and consistency of these devices. We designed a phase-change heterostructure (PCH) that consists of alternately stacked phase-change and confinement nanolayers to suppress the noise and drift, allowing reliable iterative RESET and cumulative SET operations for high-performance neuro-inspired computing. Our PCH architecture is amenable to industrial production as an intrinsic materials solution, without complex manufacturing procedure or much increased fabrication cost.
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