Integrated In‐Memory Sensor and Computing of Artificial Vision Based on Full‐vdW Optoelectronic Ferroelectric Field‐Effect Transistor
The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitra...
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Published in | Advanced science Vol. 11; no. 3; pp. e2305679 - n/a |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
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Germany
John Wiley & Sons, Inc
01.01.2024
Wiley |
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Abstract | The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h‐BN/CuInP2S6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing.
A novel multi‐functional neuromorphic visual system with optoelectronic synergy based on SnS2/BN/CuInP2S6 full van der Waals ferroelectric field‐effect transistor is reported. The device demonstrates a high switching ratio of 105, multilevel storage states of 128 (7 bits), excellent synaptic plasticity, and an image recognition accuracy of 93.62% based on reservoir computing. |
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AbstractList | The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS
2
/h‐BN/CuInP
2
S
6
(CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 10
5
, long retention time (>10
4
s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing. The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h‐BN/CuInP2S6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing. A novel multi‐functional neuromorphic visual system with optoelectronic synergy based on SnS2/BN/CuInP2S6 full van der Waals ferroelectric field‐effect transistor is reported. The device demonstrates a high switching ratio of 105, multilevel storage states of 128 (7 bits), excellent synaptic plasticity, and an image recognition accuracy of 93.62% based on reservoir computing. The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h-BN/CuInP2S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing. Abstract The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2/h‐BN/CuInP2S6 (CIPS)‐based ferroelectric field‐effect transistors (Fe‐FETs) and utilized light‐induced ferroelectric polarization reversal to achieve excellent memory properties and multi‐functional sensing‐memory‐computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105, long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7‐bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP), long‐term potentiation, and long‐term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina‐like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing‐memory‐processing. The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS /h-BN/CuInP S (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 10 , long retention time (>10 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing. The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing.The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems integrated with sensing, memory, and neuromorphic computing functions. The 2D van der Waals (vdW) materials with abundant reservoirs for arbitrary stacking based on functions and enabling continued device downscaling offer an attractive alternative for continuously promoting artificial intelligence. In this study, full 2D SnS2 /h-BN/CuInP2 S6 (CIPS)-based ferroelectric field-effect transistors (Fe-FETs) and utilized light-induced ferroelectric polarization reversal to achieve excellent memory properties and multi-functional sensing-memory-computing vision simulations are designed. The device exhibits a high on/off current ratio of over 105 , long retention time (>104 s), stable cyclic endurance (>350 cycles), and 128 multilevel current states (7-bit). In addition, fundamental synaptic plasticity characteristics are emulated including paired-pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), long-term potentiation, and long-term depression. A ferroelectric optoelectronic reservoir computing system for the Modified National Institute of Standards and Technology (MNIST) handwritten digital recognition achieved a high accuracy of 93.62%. Furthermore, retina-like light adaptation and Pavlovian conditioning are successfully mimicked. These results provide a strategy for developing a multilevel memory and novel neuromorphic vision systems with integrated sensing-memory-processing. |
Author | Wang, Peng Ci, Wenjuan Jiang, Fengxian Li, Jie Zhou, Feichi Xue, Wuhong Shi, Lei Xu, Xiaohong Zhou, Peng |
Author_xml | – sequence: 1 givenname: Peng surname: Wang fullname: Wang, Peng organization: Shanxi Normal University – sequence: 2 givenname: Jie surname: Li fullname: Li, Jie organization: Southern University of Science and Technology – sequence: 3 givenname: Wuhong surname: Xue fullname: Xue, Wuhong email: xuewuhong@sxnu.edu.cn organization: Shanxi Normal University – sequence: 4 givenname: Wenjuan surname: Ci fullname: Ci, Wenjuan organization: Shanxi Normal University – sequence: 5 givenname: Fengxian surname: Jiang fullname: Jiang, Fengxian organization: Shanxi Normal University – sequence: 6 givenname: Lei surname: Shi fullname: Shi, Lei organization: Shanxi Normal University – sequence: 7 givenname: Feichi surname: Zhou fullname: Zhou, Feichi email: zhoufc@sustech.edu.cn organization: Southern University of Science and Technology – sequence: 8 givenname: Peng orcidid: 0000-0002-7301-1013 surname: Zhou fullname: Zhou, Peng email: pengzhou@fudan.edu.cn organization: Fudan University – sequence: 9 givenname: Xiaohong orcidid: 0000-0001-7588-4793 surname: Xu fullname: Xu, Xiaohong email: xuxh@sxnu.edu.cn organization: Shanxi Normal University |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38029338$$D View this record in MEDLINE/PubMed |
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Snippet | The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing systems... The development and application of artificial intelligence have led to the exploitation of low-power and compact intelligent information-processing systems... Abstract The development and application of artificial intelligence have led to the exploitation of low‐power and compact intelligent information‐processing... |
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SubjectTerms | Energy efficiency Ferroelectrics full‐vdW ferroelectric field effect transistor Interfaces Light Microscopy multilevel memory neuromorphic vision system Retina sensing‐memory‐computing Sensors Vision systems |
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Title | Integrated In‐Memory Sensor and Computing of Artificial Vision Based on Full‐vdW Optoelectronic Ferroelectric Field‐Effect Transistor |
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