记忆电阻器
铟
瓶颈
可扩展性
神经形态工程学
材料科学
冯·诺依曼建筑
范德瓦尔斯力
计算机科学
光电子学
兴奋剂
硫化物
纳米技术
物理
电气工程
人工神经网络
人工智能
工程类
嵌入式系统
量子力学
分子
冶金
数据库
操作系统
作者
Yesheng Li,Yao Xiong,Baoxing Zhai,Lei Yin,Yiling Yu,Hao Wang,Jun He
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2024-03-13
卷期号:10 (11)
被引量:1
标识
DOI:10.1126/sciadv.adk9474
摘要
Memristors are considered promising energy-efficient artificial intelligence hardware, which can eliminate the von Neumann bottleneck by parallel in-memory computing. The common imperfection-enabled memristors are plagued with critical variability issues impeding their commercialization. Reported approaches to reduce the variability usually sacrifice other performances, e.g., small on/off ratios and high operation currents. Here, we demonstrate an unconventional Ag-doped nonimperfection diffusion channel–enabled memristor in van der Waals indium phosphorus sulfide, which can combine ultralow variabilities with desirable metrics. We achieve operation voltage, resistance, and on/off ratio variations down to 3.8, 2.3, and 6.9% at their extreme values of 0.2 V, 10 11 ohms, and 10 8 , respectively. Meanwhile, the operation current can be pushed from 1 nA to 1 pA at the scalability limit of 6 nm after Ag doping. Fourteen Boolean logic functions and convolutional image processing are successfully implemented by the memristors, manifesting the potential for logic-in-memory devices and efficient non–von Neumann accelerators.
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