All-ferroelectric implementation of reservoir computing
Abstract Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due t...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-39371-y |
| _version_ | 1797801409936097280 |
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| author | Zhiwei Chen Wenjie Li Zhen Fan Shuai Dong Yihong Chen Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu |
| author_facet | Zhiwei Chen Wenjie Li Zhen Fan Shuai Dong Yihong Chen Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu |
| author_sort | Zhiwei Chen |
| collection | DOAJ |
| description | Abstract Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO3/SrRuO3 structure via the manipulation of an imprint field (E imp). It is shown that the volatile FD with E imp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E imp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing. |
| first_indexed | 2024-03-13T04:50:03Z |
| format | Article |
| id | doaj.art-f5fbc3522c444e9b9aa38688c6805090 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-13T04:50:03Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-f5fbc3522c444e9b9aa38688c68050902023-06-18T11:18:38ZengNature PortfolioNature Communications2041-17232023-06-0114111210.1038/s41467-023-39371-yAll-ferroelectric implementation of reservoir computingZhiwei Chen0Wenjie Li1Zhen Fan2Shuai Dong3Yihong Chen4Minghui Qin5Min Zeng6Xubing Lu7Guofu Zhou8Xingsen Gao9Jun-Ming Liu10Institute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityNational Center for International Research on Green Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityInstitute for Advanced Materials and Guangdong Provincial Key Laboratory of Optical Information Materials and Technology, South China Academy of Advanced Optoelectronics, South China Normal UniversityAbstract Reservoir computing (RC) offers efficient temporal information processing with low training cost. All-ferroelectric implementation of RC is appealing because it can fully exploit the merits of ferroelectric memristors (e.g., good controllability); however, this has been undemonstrated due to the challenge of developing ferroelectric memristors with distinctly different switching characteristics specific to the reservoir and readout network. Here, we experimentally demonstrate an all-ferroelectric RC system whose reservoir and readout network are implemented with volatile and nonvolatile ferroelectric diodes (FDs), respectively. The volatile and nonvolatile FDs are derived from the same Pt/BiFeO3/SrRuO3 structure via the manipulation of an imprint field (E imp). It is shown that the volatile FD with E imp exhibits short-term memory and nonlinearity while the nonvolatile FD with negligible E imp displays long-term potentiation/depression, fulfilling the functional requirements of the reservoir and readout network, respectively. Hence, the all-ferroelectric RC system is competent for handling various temporal tasks. In particular, it achieves an ultralow normalized root mean square error of 0.017 in the Hénon map time-series prediction. Besides, both the volatile and nonvolatile FDs demonstrate long-term stability in ambient air, high endurance, and low power consumption, promising the all-ferroelectric RC system as a reliable and low-power neuromorphic hardware for temporal information processing.https://doi.org/10.1038/s41467-023-39371-y |
| spellingShingle | Zhiwei Chen Wenjie Li Zhen Fan Shuai Dong Yihong Chen Minghui Qin Min Zeng Xubing Lu Guofu Zhou Xingsen Gao Jun-Ming Liu All-ferroelectric implementation of reservoir computing Nature Communications |
| title | All-ferroelectric implementation of reservoir computing |
| title_full | All-ferroelectric implementation of reservoir computing |
| title_fullStr | All-ferroelectric implementation of reservoir computing |
| title_full_unstemmed | All-ferroelectric implementation of reservoir computing |
| title_short | All-ferroelectric implementation of reservoir computing |
| title_sort | all ferroelectric implementation of reservoir computing |
| url | https://doi.org/10.1038/s41467-023-39371-y |
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