Xing Deng: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Yu-Xiang Liu: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Zhen-Zhong Yang: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Yi-Feng Zhao: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Ya-Ting Xu: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.
Meng-Yao Fu: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.
Yu Shen: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.
Ke Qu: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.
Zhao Guan: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China.
Wen-Yi Tong: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Yuan-Yuan Zhang: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Bin-Bin Chen: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Ni Zhong: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Ping-Hua Xiang: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
Chun-Gang Duan: Key Laboratory of Polar Materials and Devices (Ministry of Education), Shanghai Center of Brain-Inspired Intelligent Materials and Devices, Department of Electronics, East China Normal University, Shanghai 200241, China. ORCID
The proton-electron coupling effect induces rich spectrums of electronic states in correlated oxides, opening tempting opportunities for exploring novel devices with multifunctions. Here, via modest Pt-aided hydrogen spillover at room temperature, amounts of protons are introduced into SmNiO-based devices. In situ structural characterizations together with first-principles calculation reveal that the local Mott transition is reversibly driven by migration and redistribution of the predoped protons. The accompanying giant resistance change results in excellent memristive behaviors under ultralow electric fields. Hierarchical tree-like memory states, an instinct displayed in bio-synapses, are further realized in the devices by spatially varying the proton concentration with electric pulses, showing great promise in artificial neural networks for solving intricate problems. Our research demonstrates the direct and effective control of proton evolution using extremely low electric field, offering an alternative pathway for modifying the functionalities of correlated oxides and constructing low-power consumption intelligent devices and neural network circuits.
References
Nature. 2016 May 16;534(7606):231-4
[PMID: 27279218]
