Ultra robust negative differential resistance memristor for hardware neuron circuit implementation
Abstract Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological n...
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Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-55293-9 |
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| author | Yifei Pei Biao Yang Xumeng Zhang Hui He Yong Sun Jianhui Zhao Pei Chen Zhanfeng Wang Niefeng Sun Shixiong Liang Guodong Gu Qi Liu Shushen Li Xiaobing Yan |
| author_facet | Yifei Pei Biao Yang Xumeng Zhang Hui He Yong Sun Jianhui Zhao Pei Chen Zhanfeng Wang Niefeng Sun Shixiong Liang Guodong Gu Qi Liu Shushen Li Xiaobing Yan |
| author_sort | Yifei Pei |
| collection | DOAJ |
| description | Abstract Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing. Here, we propose an ultra-robust and efficient neuron of negative differential resistance (NDR) memristor based on AlAs/In0.8Ga0.2As/AlAs quantum well (QW) structure, which has super stable performance such as low variation (0.264%), high temperature resistance (400 °C) and high endurance. The NDR devices can cycle more than 1011 switching cycles at room temperature and more than 109 switching cycles even at a high temperature of 400 °C, which means that the device can operate for more than 310 years at 10 Hz update frequency. Furthermore, the NDR memristor implements the integration feature of the neuronal membrane and avoids using external capacitors, and successfully apply it to the self-designed super reduced neuron circuit. Moreover, we have successfully constructed Fitz Hugh Nagumo (FN) neuron circuit, reduced hardware costs of FN neuron circuit and enabling diverse neuron dynamics and nine neuron functions. Meanwhile, based on the high temperature stability of the device, a voltage-temperature fused multimodal impulse neural network was constructed to achieve 91.74% accuracy in classifying digital images with different temperature labels. This work offers a novel approach to build FN neuron circuits using NDR memristors, and provides a more competitive method to build a highly reliable neuromorphic hardware system. |
| format | Article |
| id | doaj-art-5c4ce00e7d014815b4360711c95e09b0 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-5c4ce00e7d014815b4360711c95e09b02025-01-05T12:40:20ZengNature PortfolioNature Communications2041-17232025-01-0116111010.1038/s41467-024-55293-9Ultra robust negative differential resistance memristor for hardware neuron circuit implementationYifei Pei0Biao Yang1Xumeng Zhang2Hui He3Yong Sun4Jianhui Zhao5Pei Chen6Zhanfeng Wang7Niefeng Sun8Shixiong Liang9Guodong Gu10Qi Liu11Shushen Li12Xiaobing Yan13Key Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei UniversityCollege of Electronic and Information Engineering, Hebei UniversityFrontier Institute of Chip and System, Fudan UniversityKey Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei UniversityCollege of Electronic and Information Engineering, Hebei UniversityCollege of Electronic and Information Engineering, Hebei UniversityFrontier Institute of Chip and System, Fudan UniversityKey Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei UniversityNational Key Laboratory of Solid-state Microwave Devices and Circuits, Hebei Semiconductor Research InstituteSchool of Microelectronics, Tianjin UniversityNational Key Laboratory of Solid-state Microwave Devices and Circuits, Hebei Semiconductor Research InstituteFrontier Institute of Chip and System, Fudan UniversityKey Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei UniversityKey Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei UniversityAbstract Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing. Here, we propose an ultra-robust and efficient neuron of negative differential resistance (NDR) memristor based on AlAs/In0.8Ga0.2As/AlAs quantum well (QW) structure, which has super stable performance such as low variation (0.264%), high temperature resistance (400 °C) and high endurance. The NDR devices can cycle more than 1011 switching cycles at room temperature and more than 109 switching cycles even at a high temperature of 400 °C, which means that the device can operate for more than 310 years at 10 Hz update frequency. Furthermore, the NDR memristor implements the integration feature of the neuronal membrane and avoids using external capacitors, and successfully apply it to the self-designed super reduced neuron circuit. Moreover, we have successfully constructed Fitz Hugh Nagumo (FN) neuron circuit, reduced hardware costs of FN neuron circuit and enabling diverse neuron dynamics and nine neuron functions. Meanwhile, based on the high temperature stability of the device, a voltage-temperature fused multimodal impulse neural network was constructed to achieve 91.74% accuracy in classifying digital images with different temperature labels. This work offers a novel approach to build FN neuron circuits using NDR memristors, and provides a more competitive method to build a highly reliable neuromorphic hardware system.https://doi.org/10.1038/s41467-024-55293-9 |
| spellingShingle | Yifei Pei Biao Yang Xumeng Zhang Hui He Yong Sun Jianhui Zhao Pei Chen Zhanfeng Wang Niefeng Sun Shixiong Liang Guodong Gu Qi Liu Shushen Li Xiaobing Yan Ultra robust negative differential resistance memristor for hardware neuron circuit implementation Nature Communications |
| title | Ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| title_full | Ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| title_fullStr | Ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| title_full_unstemmed | Ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| title_short | Ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| title_sort | ultra robust negative differential resistance memristor for hardware neuron circuit implementation |
| url | https://doi.org/10.1038/s41467-024-55293-9 |
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