Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption
Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endura...
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Elsevier
2025-09-01
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| Series: | Journal of Materiomics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352847825000413 |
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| author | Weifeng Zhang Jikang Xu Yongrui Wang Yinxing Zhang Yu Wang Pengfei Li Yongqing Jia Zhen Zhao Changliang Li Biao Yang Yue Hou Zhenqiang Guo Zeze Huang Yincheng Qi Xiaobing Yan |
| author_facet | Weifeng Zhang Jikang Xu Yongrui Wang Yinxing Zhang Yu Wang Pengfei Li Yongqing Jia Zhen Zhao Changliang Li Biao Yang Yue Hou Zhenqiang Guo Zeze Huang Yincheng Qi Xiaobing Yan |
| author_sort | Weifeng Zhang |
| collection | DOAJ |
| description | Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba0.6Sr0.4TiO3)0.5(Nd2O3)0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La0.67Sr0.33MnO3/SrTiO3/PSi substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm2), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 109 switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors. |
| format | Article |
| id | doaj-art-e47e29a539294dba90087b6f5f56a462 |
| institution | Matheson Library |
| issn | 2352-8478 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materiomics |
| spelling | doaj-art-e47e29a539294dba90087b6f5f56a4622025-06-29T04:52:25ZengElsevierJournal of Materiomics2352-84782025-09-01115101051Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryptionWeifeng Zhang0Jikang Xu1Yongrui Wang2Yinxing Zhang3Yu Wang4Pengfei Li5Yongqing Jia6Zhen Zhao7Changliang Li8Biao Yang9Yue Hou10Zhenqiang Guo11Zeze Huang12Yincheng Qi13Xiaobing Yan14Institute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaDepartment of Electronic and Communication Engineering, Intelligent Vision Computing Research Institute, North China Electric Power University, Baoding, 071003, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaHebei Key Lab of Optic-Electronic Information and Materials, College of Physics Science & Technology, Baoding, 071002, Hebei, ChinaHebei Key Lab of Optic-Electronic Information and Materials, College of Physics Science & Technology, Baoding, 071002, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, ChinaHebei Key Lab of Optic-Electronic Information and Materials, College of Physics Science & Technology, Baoding, 071002, Hebei, ChinaHebei Key Lab of Optic-Electronic Information and Materials, College of Physics Science & Technology, Baoding, 071002, Hebei, ChinaDepartment of Electronic and Communication Engineering, Intelligent Vision Computing Research Institute, North China Electric Power University, Baoding, 071003, Hebei, ChinaDepartment of Electronic and Communication Engineering, Intelligent Vision Computing Research Institute, North China Electric Power University, Baoding, 071003, Hebei, ChinaInstitute of Life Science and Green Development, Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Baoding, 071002, Hebei, China; Corresponding author.Recent advancements in AI have spurred interest in ferroelectric memristors for neuromorphic chips due to their ability to precisely control resistive states through polarization flip-flop without electroforming. However, oxygen vacancies in these devices often cause high leakage current, low endurance, and dispersed switching voltages. Here, we introduce a silicon-based integrated (Ba0.6Sr0.4TiO3)0.5(Nd2O3)0.5 (BSTN) nanoscaffolded ferroelectric thin film memristor with a vertically self-assembled nanocomposite structure (VSNs) optimally oriented on La0.67Sr0.33MnO3/SrTiO3/PSi substrates. This device demonstrates a widely tunable ferroelectric domain range (0°–180°), high remnant polarization (21.04 μC/cm2), and a greater number of unitary states (16 states or 4 bits). It exhibits high durability, enduring over 109 switching cycles. The switching mechanism combines ferroelectric polarization and oxygen vacancy migration, enabling the simulation of biological synaptic functions via bi-directional conductance tunability. Additionally, we implemented a low-power (0.57 pJ per event) multi-factor secure encryption system for smart locks using 16×16 BSTN memristor crossbar arrays and a pressure sensor. Under multiple factors (disordered inputs, specific users, and corresponding passwords) the system recognized passwords with 97.6% accuracy and a 3.8% loss rate after 500 iterations. Overall, this work establishes a robust foundation for advancing multilevel storage, neuromorphic computing, and AI chip applications based on ferroelectric memristors.http://www.sciencedirect.com/science/article/pii/S2352847825000413Ferroelectric memristorNanoscaffoldedInterface channelMulti-factor secure encryption system |
| spellingShingle | Weifeng Zhang Jikang Xu Yongrui Wang Yinxing Zhang Yu Wang Pengfei Li Yongqing Jia Zhen Zhao Changliang Li Biao Yang Yue Hou Zhenqiang Guo Zeze Huang Yincheng Qi Xiaobing Yan Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption Journal of Materiomics Ferroelectric memristor Nanoscaffolded Interface channel Multi-factor secure encryption system |
| title | Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| title_full | Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| title_fullStr | Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| title_full_unstemmed | Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| title_short | Nanoscaffold Ba0.6Sr0.4TiO3:Nd2O3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| title_sort | nanoscaffold ba0 6sr0 4tio3 nd2o3 ferroelectric memristors crossbar array for neuromorphic computing and secure encryption |
| topic | Ferroelectric memristor Nanoscaffolded Interface channel Multi-factor secure encryption system |
| url | http://www.sciencedirect.com/science/article/pii/S2352847825000413 |
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