Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing
As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2022-01-01
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| Series: | Research |
| Online Access: | http://dx.doi.org/10.34133/2022/9754876 |
| _version_ | 1797287778279489536 |
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| author | Zhongrong Wang Wei Wang Pan Liu Gongjie Liu Jiahang Li Jianhui Zhao Zhenyu Zhou Jingjuan Wang Yifei Pei Zhen Zhao Jiaxin Li Lei Wang Zixuan Jian Yichao Wang Jianxin Guo Xiaobing Yan |
| author_facet | Zhongrong Wang Wei Wang Pan Liu Gongjie Liu Jiahang Li Jianhui Zhao Zhenyu Zhou Jingjuan Wang Yifei Pei Zhen Zhao Jiaxin Li Lei Wang Zixuan Jian Yichao Wang Jianxin Guo Xiaobing Yan |
| author_sort | Zhongrong Wang |
| collection | DOAJ |
| description | As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a ROFF/RON ratio of ~5 × 103, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing. |
| first_indexed | 2024-03-07T18:39:48Z |
| format | Article |
| id | doaj.art-d9e8ad346363467e887de9f845ccb5dd |
| institution | Directory Open Access Journal |
| issn | 2639-5274 |
| language | English |
| last_indexed | 2024-03-07T18:39:48Z |
| publishDate | 2022-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Research |
| spelling | doaj.art-d9e8ad346363467e887de9f845ccb5dd2024-03-02T04:31:50ZengAmerican Association for the Advancement of Science (AAAS)Research2639-52742022-01-01202210.34133/2022/9754876Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic ComputingZhongrong Wang0Wei Wang1Pan Liu2Gongjie Liu3Jiahang Li4Jianhui Zhao5Zhenyu Zhou6Jingjuan Wang7Yifei Pei8Zhen Zhao9Jiaxin Li10Lei Wang11Zixuan Jian12Yichao Wang13Jianxin Guo14Xiaobing Yan15Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaDepartment of Clinical Laboratory Medicine, Taizhou Central Hospital (Taizhou University Hospital), Taizhou 318000, ChinaCollege of Physics Science and Technology, Hebei University, Baoding 071002, ChinaKey Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding 071002, ChinaAs the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a ROFF/RON ratio of ~5 × 103, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.http://dx.doi.org/10.34133/2022/9754876 |
| spellingShingle | Zhongrong Wang Wei Wang Pan Liu Gongjie Liu Jiahang Li Jianhui Zhao Zhenyu Zhou Jingjuan Wang Yifei Pei Zhen Zhao Jiaxin Li Lei Wang Zixuan Jian Yichao Wang Jianxin Guo Xiaobing Yan Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing Research |
| title | Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing |
| title_full | Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing |
| title_fullStr | Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing |
| title_full_unstemmed | Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing |
| title_short | Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing |
| title_sort | superlow power consumption artificial synapses based on wse2 quantum dots memristor for neuromorphic computing |
| url | http://dx.doi.org/10.34133/2022/9754876 |
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