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| 1 | Versatile SrFeO_(x) for memristive neurons and synapses显示文摘Spiking neural network(SNN)consisting of memristor-based artificial neurons and synapses has emerged as a compact and energy-efficient hardware solution for spatiotemporal information processing.However,it is challenging to develop memristive neurons and synapses based on the same material system because the required resistive switching(RS)characteristics are different.Here,it is shown that SrFeO_(x)(SFO),an intriguing material system exhibiting topotactic phase transformation between insulating brownmillerite(BM)SrFeO_(2).5 phase and conductive perovskite(PV)SrFeO_(3) phase,can be engineered into both neuronal and synaptic devices.Using a BM-SFO single layer as the RS medium,the Au/BM-SFO/SrRuO_(3)(SRO)memristor exhibits nonvolatile RS behavior originating from the formation/rupture of PV-SFO filaments in the BM-SFO matrix.By contrast,using a PV-SFO(matrix)/BM-SFO(interfacial layer)bilayer as the RS medium,the Au/PV-SFO/BM-SFO/SRO memristor exhibits volatile RS behavior originating from the interfacial BM-PV phase transformation.Synaptic and neuronal characteristics are further demonstrated in the Au/BM-SFO/SRO and Au/PV-SFO/BM-SFO/SRO memristors,respectively.Using the SFO-based synapses and neurons,fully memristive SNNs are constructed by simulation,which show good performance on unsupervised image recognition.Our study suggests that SFO is a versatile material platform on which both neuronal and synaptic devices can be developed for constructing fully memristive SNNs. | Kaihui Chen Zhen Fan Jingjing Rao Wenjie Li Deming Wang Changjian Li Gaokuo Zhong Ruiqiang Tao Guo Tian Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu | 2022 | Journal of Materiomics2022，8，5： | 0 |
| 2 | AVA Simultaneous Inversion of Prestack Seismic Data Using Particle Swarm Optimization显示文摘A new prestack AVA simultaneous inversion using particle swarm optimization algorithm is proposed, which can obtain the elastic parameters such as P-wave and S-wave impedance from P-wave reflection data simultaneously. Compared with the conventional AVA inversion based on generalized linear technique, this method does not depend on the initial model and can reach the global minimum. In order to increase the stability of the inversion, low-frequency trends of P-wave and S-wave impedances are built into the inversion. This method has been successfully applied to synthetic and field data. The estimated P-wave and S-wave impedances can be combined to derive other elastic parameters, which are sensitive for lithology identification and fluid prediction. | Jin Zhang Peng Shen Weina Zhao Xubing Guo Xing Wang Song Chen Xiugang Xu | 2018 | Journal of Earth Science2018，29，6： | 0 |
| 3 | Experimental search for high-performance ferroelectric tunnel junctions guided by machine learning显示文摘Ferroelectric tunnel junction(FTJ)has attracted considerable attention for its potential applications in nonvolatile memory and neuromorphic computing.However,the experimental exploration of FTJs with high ON/OFF ratios is a challenging task due to the vast search space comprising of ferroelectric and electrode materials,fabrication methods and conditions and so on.Here,machine learning(ML)is demonstrated to be an effective tool to guide the experimental search of FTJs with high ON/OFF ratios.A dataset consisting of 152 FTJ samples with nine features and one target attribute(i.e.,ON/OFF ratio)is established for ML modeling.Among various ML models,the gradient boosting classification model achieves the highest prediction accuracy.Combining the feature importance analysis based on this model with the association rule mining,it is extracted that the utilizations of{graphene/graphite(Gra)(top),LaNiO_(3)(LNO)(bottom)}and{Gra(top),Ca_(0.96)Ce_(0.04)MnO_(3)(CCMO)(bottom)}electrode pairs are likely to result in high ON/OFF ratios in FTJs.Moreover,two previously unexplored FTJs:Gra/BaTiO_(3)(BTO)/LNO and Gra/BTO/CCMO,are predicted to achieve ON/OFF ratios higher than 1000.Guided by the ML predictions,the Gra/BTO/LNO and Gra/BTO/CCMO FTJs are experimentally fabricated,which unsurprisingly exhibit≥1000 ON/OFF ratios(~8540 and~7890,respectively).This study demonstrates a new paradigm of developing high-performance FTJs by using ML. | Jingjing Rao Zhen Fan Qicheng Huang Yongjian Luo Xingmin Zhang Haizhong Guo Xiaobing Yan Guo Tian Deyang Chen Zhipeng Hou Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu | 2022 | Journal of Advanced Dielectrics2022，12，3： | 0 |
| 4 | A flexible and high temperature tolerant strain sensor of La0.7Sr0.3MnO3/Mica显示文摘Flexible sensors have been widely investigated due to their broad application prospects in various flexible electronics.However,most of the presently studied flexible sensors are only suitable for working at room temperature,and their applications at high or low temperatures are still a big challenge.In this work,we present a multimodal flexible sensor based on functional oxide La0.7Sr0.3MnO3(LSMO)thin film deposited on mica substrate.As a strain sensor,it shows excellent sensitivity to mechanical bending and high bending durability(up to 3600 cycles).Moreover,the LSMO/Mica sensor also shows a sensitive response to the magnetic field,implying its multimodal sensing ability.Most importantly,it can work in a wide temperature range from extreme low temperature down to 20K to high temperature up to 773 K.The flexible sensor based on the flexible LSMO/mica hetero-structure shows great potential applications for flexible electronics using at extreme temperature environment in the future. | Min Guo Cheng Yang Dong Gao Qiang Li Aihua Zhang Jiajun Feng Hui Yang Ruiqiang Tao Zhen Fan Min Zeng Guofu Zhou Xubing Lu J-MLiu | 2020 | Journal of Materials Science & Technology2020，44，9： | 0 |
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