A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems
The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorph...
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| Vydáno v: | Nature communications Ročník 12; číslo 1; s. 1798 - 7 |
|---|---|
| Hlavní autoři: | , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
Nature Publishing Group UK
19.03.2021
Nature Publishing Group Nature Portfolio |
| Témata: | |
| ISSN: | 2041-1723, 2041-1723 |
| On-line přístup: | Získat plný text |
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| Abstract | The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10
7
A/W and a specific detectivity of 2 × 10
16
Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm
2
.
To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning. |
|---|---|
| AbstractList | The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10
A/W and a specific detectivity of 2 × 10
Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm
. The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2.The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2. To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning. The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10 7 A/W and a specific detectivity of 2 × 10 16 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm 2 . To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning. The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2.To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning. The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 107 A/W and a specific detectivity of 2 × 1016 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm2. To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report flexible sensor array of 1024 pixels using combination of carbon nanotubes and perovskite QDs as active matetials, achieving highly responsive device for reinforcement learning. The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and adaptability of animal systems, but also how to do so with computational efficiency and elegance. Similar to biological systems, these neuromorphic circuits integrate functions of image sensing, memory and processing into the device, and process continuous analog brightness signal in real-time. High-integration, flexibility and ultra-sensitivity are essential for practical artificial vision systems that attempt to emulate biological processing. Here, we present a flexible optoelectronic sensor array of 1024 pixels using a combination of carbon nanotubes and perovskite quantum dots as active materials for an efficient neuromorphic vision system. The device has an extraordinary sensitivity to light with a responsivity of 5.1 × 10 7 A/W and a specific detectivity of 2 × 10 16 Jones, and demonstrates neuromorphic reinforcement learning by training the sensor array with a weak light pulse of 1 μW/cm 2 . |
| ArticleNumber | 1798 |
| Author | Chen, Mao-Lin Zhu, Qian-Bing Cheng, Hui-Ming Sun, Dong-Ming Zeng, Hai-Bo Yang, Dan-Dan Feng, Shun Li, Qing-Wen Su, Xin Li, Bo Liu, Chi Wang, Xiao-Mu Li, Xiao-Ming Tian, Ya-Nan Sun, Yun Qiu, Song |
| Author_xml | – sequence: 1 givenname: Qian-Bing surname: Zhu fullname: Zhu, Qian-Bing organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Material Science and Engineering, University of Science and Technology of China – sequence: 2 givenname: Bo surname: Li fullname: Li, Bo organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Material Science and Engineering, University of Science and Technology of China – sequence: 3 givenname: Dan-Dan surname: Yang fullname: Yang, Dan-Dan organization: College of Materials Science and Engineering, Nanjing University of Science and Technology – sequence: 4 givenname: Chi orcidid: 0000-0002-8778-3831 surname: Liu fullname: Liu, Chi organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 5 givenname: Shun surname: Feng fullname: Feng, Shun organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Physical Science and Technology, ShanghaiTech University – sequence: 6 givenname: Mao-Lin orcidid: 0000-0001-7126-9224 surname: Chen fullname: Chen, Mao-Lin organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 7 givenname: Yun surname: Sun fullname: Sun, Yun organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences – sequence: 8 givenname: Ya-Nan surname: Tian fullname: Tian, Ya-Nan organization: College of Information Science and Engineering, Northeastern University – sequence: 9 givenname: Xin surname: Su fullname: Su, Xin organization: School of Electronic Science and Engineering, Nanjing University – sequence: 10 givenname: Xiao-Mu orcidid: 0000-0001-8975-5626 surname: Wang fullname: Wang, Xiao-Mu organization: School of Electronic Science and Engineering, Nanjing University – sequence: 11 givenname: Song orcidid: 0000-0001-6965-4940 surname: Qiu fullname: Qiu, Song email: sqiu2010@sinano.ac.cn organization: Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences – sequence: 12 givenname: Qing-Wen surname: Li fullname: Li, Qing-Wen organization: Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences – sequence: 13 givenname: Xiao-Ming surname: Li fullname: Li, Xiao-Ming email: lixiaoming@njust.edu.cn organization: College of Materials Science and Engineering, Nanjing University of Science and Technology – sequence: 14 givenname: Hai-Bo orcidid: 0000-0002-0260-1059 surname: Zeng fullname: Zeng, Hai-Bo organization: College of Materials Science and Engineering, Nanjing University of Science and Technology – sequence: 15 givenname: Hui-Ming orcidid: 0000-0002-5387-4241 surname: Cheng fullname: Cheng, Hui-Ming email: cheng@imr.ac.cn organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Material Science and Engineering, University of Science and Technology of China, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University – sequence: 16 givenname: Dong-Ming orcidid: 0000-0003-1552-7940 surname: Sun fullname: Sun, Dong-Ming email: dmsun@imr.ac.cn organization: Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, School of Material Science and Engineering, University of Science and Technology of China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33741964$$D View this record in MEDLINE/PubMed |
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| Snippet | The challenges of developing neuromorphic vision systems inspired by the human eye come not only from how to recreate the flexibility, sophistication, and... To emulate nature biological processing, highly-integrated ultra-sensitive artificial neuromorphic system is highly desirable. Here, the authors report... |
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| Title | A flexible ultrasensitive optoelectronic sensor array for neuromorphic vision systems |
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