记忆电阻器
材料科学
横杆开关
神经形态工程学
光电子学
掺杂剂
兴奋剂
电铸
电阻随机存取存储器
纳米技术
电子工程
人工神经网络
计算机科学
图层(电子)
电压
电气工程
人工智能
工程类
作者
Sung‐Eun Kim,Jin‐Gyu Lee,Leo Ling,Stephanie E. Liu,Hyung‐Kyu Lim,Vinod K. Sangwan,Mark C. Hersam,Hong‐Sub Lee
标识
DOI:10.1002/adma.202106913
摘要
Memristors integrated into a crossbar-array architecture (CAA) are promising candidates for nonvolatile memory elements in artificial neural networks. However, the relatively low reliability of memristors coupled with crosstalk and sneak currents in CAAs have limited the realization of the full potential of this technology. Here, high-reliability Na-doped TiO2 memristors grown in situ by atomic layer deposition (ALD) are demonstrated, where reversible Na migration underlies the resistive-switching mechanism. By employing ALD growth with an aqueous NaOH reactant in deionized water, uniform implantation of Na dopants is achieved in the crystallized TiO2 thin films at 250 °C without post-annealing. The resulting Na-doped TiO2 memristors show electroforming-free and self-rectifying resistive-switching behavior, and they are ideally suited for selectorless CAAs. Effective addressing of selectorless nodes is demonstrated via electrical measurement of individual memristors in a 6 × 6 crossbar using a read current of less than 1 µA with negligible sneak current at or below the noise level of ≈100 pA. Finally, the long-term potentiation and depression synaptic behavior from these Na-doped TiO2 memristors achieves greater than 99.1% accuracy for image-recognition tasks using a convolutional neural network based on the selectorless of crossbar arrays.
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