记忆电阻器
横杆开关
神经形态工程学
材料科学
记忆晶体管
人工神经网络
突触重量
欧姆接触
相(物质)
乘法(音乐)
CMOS芯片
电阻随机存取存储器
拓扑(电路)
电子工程
计算机科学
光电子学
电压
纳米技术
电气工程
数学
人工智能
物理
工程类
图层(电子)
量子力学
组合数学
作者
Sumin Kim,Sungkyu Kim,Leo Ling,Stephanie E. Liu,Sila Jin,Young Mee Jung,Minjae Kim,Hyung Ho Park,Vinod K. Sangwan,Mark C. Hersam,Hong Sub Lee
标识
DOI:10.1021/acsami.1c24562
摘要
Emerging energy-efficient neuromorphic circuits are based on hardware implementation of artificial neural networks (ANNs) that employ the biomimetic functions of memristors. Specifically, crossbar array memristive architectures are able to perform ANN vector-matrix multiplication more efficiently than conventional CMOS hardware. Memristors with specific characteristics, such as ohmic behavior in all resistance states in addition to symmetric and linear long-term potentiation/depression (LTP/LTD), are required in order to fully realize these benefits. Here, we demonstrate a Li-based composite memristor (LCM) that achieves these objectives. The LCM consists of three phases: Li-doped TiO2 as a Li reservoir, Li4Ti5O12 as the insulating phase, and Li7Ti5O12 as the metallic phase, where resistive switching correlates with the change in the relative fraction of the metallic and insulating phases. The LCM exhibits a symmetric and gradual resistive switching behavior for both set and reset operations during a full bias sweep cycle. This symmetric and linear weight update is uniquely enabled by the symmetric bidirectional migration of Li ions, which leads to gradual changes in the relative fraction of the metallic phase in the film. The optimized LCM in ANN simulation showed that exceptionally high accuracy in image classification is realized in fewer training steps compared to the nonlinear behavior of conventional memristors.
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