An electroforming-free, analog interface-type memristor based on a SrFeOx epitaxial heterojunction for neuromorphic computing
| Autor: | Xingsen Gao, Haizhong Guo, Shengliang Cheng, Lanqing Hong, Jin Zhao, Er-Jia Guo, Zhipeng Hou, Jingjing Rao, Y. Chen, Zhen Fan, X. Xiang, Xubing Lu, Jun-Ming Liu, Guofu Zhou, Qicheng Huang |
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| Rok vydání: | 2021 |
| Předmět: |
Materials science
Physics and Astronomy (miscellaneous) business.industry Phase (waves) Conductance Heterojunction 02 engineering and technology Memristor 010402 general chemistry 021001 nanoscience & nanotechnology 01 natural sciences 0104 chemical sciences law.invention Neuromorphic engineering Modulation law Electroforming Optoelectronics General Materials Science 0210 nano-technology business Energy (miscellaneous) Electronic circuit |
| Zdroj: | Materials Today Physics. 18:100392 |
| ISSN: | 2542-5293 |
| Popis: | Distinct from the conductive filament-type counterparts, the interface-type resistive switching (RS) devices are electroforming-free and exhibit bidirectionally continuous conductance changes, making them promising candidates as analog synapses. While the interface-type RS devices typically operate through the interfacial oxygen migration, materials which can tolerate a wide range of oxygen non-stoichiometry and possess high oxygen mobility are therefore demanded. SrFeOx (SFO), which can easily transform between a conductive, oxygenated perovskite SrFeO3 (PV-SFO) phase and an insulating, oxygen-vacancy-rich brownmillerite SrFeO2.5 (BM-SFO) phase under electric field, emerges as a suitable material. Herein, an interface-type RS device is ingeniously structured by two epitaxial SFO layers: a PV-SFO matrix layer and an ultrathin BM-SFO interfacial layer, aiming to leverage the oxygen migration-induced interfacial BM-PV phase transformation to realize the gradual conductance modulation. Experimentally, the fabricated device exhibits electroforming-free, analog memristive behavior. This device also emulates essential synaptic functions, including excitatory postsynaptic current, paired-pulse facilitation, transition from short-term memory to long-term memory, spike-timing-dependent plasticity, and potentiation/depression. A simulated neural network built from the SFO-based synapses achieves accuracies above 88% for image recognition. This work provides a novel approach to use the SFO family of topotactic materials for developing analog synapses as building blocks for neuromorphic computing circuits. |
| Databáze: | OpenAIRE |
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