Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation
Abstract Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics, optics, and electronics. Among different types of heterostructures, the gradient one may provide multiple resistive states and immobilized condu...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
|
| Series: | InfoMat |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/inf2.12351 |
| _version_ | 1797939141498896384 |
|---|---|
| author | Changjiu Teng Qiangmin Yu Yujie Sun Baofu Ding Wenjun Chen Zehao Zhang Bilu Liu Hui‐Ming Cheng |
| author_facet | Changjiu Teng Qiangmin Yu Yujie Sun Baofu Ding Wenjun Chen Zehao Zhang Bilu Liu Hui‐Ming Cheng |
| author_sort | Changjiu Teng |
| collection | DOAJ |
| description | Abstract Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics, optics, and electronics. Among different types of heterostructures, the gradient one may provide multiple resistive states and immobilized conductive filaments, offering great prospect for fabricating memristors with both high neuromorphic computation capability and repeatability. Here, we invent a memristor based on a homologous gradient heterostructure (HGHS), comprising a conductive transition metal dichalcogenide and an insulating homologous metal oxide. Memristor made of Ta–TaSxOy–TaS2 HGHS exhibits continuous potentiation/depression behavior and repeatable forward/backward scanning in the read‐voltage range, which are dominated by multiple resistive states and immobilized conductive filaments in HGHS, respectively. Moreover, the continuous potentiation/depression behavior makes the memristor serve as a synapse, featuring broad‐frequency response (10−1–105 Hz, covering 106 frequency range) and multiple‐mode learning (enhanced, depressed, and random‐level modes) based on its natural and motivated forgetting behaviors. Such HGHS‐based memristor also shows good uniformity for 5 × 7 device arrays. Our work paves a way to achieve high‐performance integrated memristors for future artificial neuromorphic computation. |
| first_indexed | 2024-04-10T19:11:52Z |
| format | Article |
| id | doaj.art-227f71e90c3549479834bf2374c3b53d |
| institution | Directory Open Access Journal |
| issn | 2567-3165 |
| language | English |
| last_indexed | 2024-04-10T19:11:52Z |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | InfoMat |
| spelling | doaj.art-227f71e90c3549479834bf2374c3b53d2023-01-30T13:26:47ZengWileyInfoMat2567-31652023-01-0151n/an/a10.1002/inf2.12351Homologous gradient heterostructure‐based artificial synapses for neuromorphic computationChangjiu Teng0Qiangmin Yu1Yujie Sun2Baofu Ding3Wenjun Chen4Zehao Zhang5Bilu Liu6Hui‐Ming Cheng7Shenzhen Geim Graphene Center, Tsinghua−Berkeley Shenzhen Institute and Institute of Materials Research, Shenzhen International Graduate School Tsinghua University Shenzhen People's Republic of ChinaShenzhen Geim Graphene Center, Tsinghua−Berkeley Shenzhen Institute and Institute of Materials Research, Shenzhen International Graduate School Tsinghua University Shenzhen People's Republic of ChinaShenzhen Geim Graphene Center, Tsinghua−Berkeley Shenzhen Institute and Institute of Materials Research, Shenzhen International Graduate School Tsinghua University Shenzhen People's Republic of ChinaFaculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology, Chinese Academy of Science Shenzhen People's Republic of ChinaSchool of Electronic and Information Engineering Foshan University Foshan People's Republic of ChinaShenzhen Geim Graphene Center, Tsinghua−Berkeley Shenzhen Institute and Institute of Materials Research, Shenzhen International Graduate School Tsinghua University Shenzhen People's Republic of ChinaShenzhen Geim Graphene Center, Tsinghua−Berkeley Shenzhen Institute and Institute of Materials Research, Shenzhen International Graduate School Tsinghua University Shenzhen People's Republic of ChinaFaculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced Technology, Chinese Academy of Science Shenzhen People's Republic of ChinaAbstract Gradient heterostructure is one of fundamental interfaces and provides an effective platform to achieve gradually changed properties in mechanics, optics, and electronics. Among different types of heterostructures, the gradient one may provide multiple resistive states and immobilized conductive filaments, offering great prospect for fabricating memristors with both high neuromorphic computation capability and repeatability. Here, we invent a memristor based on a homologous gradient heterostructure (HGHS), comprising a conductive transition metal dichalcogenide and an insulating homologous metal oxide. Memristor made of Ta–TaSxOy–TaS2 HGHS exhibits continuous potentiation/depression behavior and repeatable forward/backward scanning in the read‐voltage range, which are dominated by multiple resistive states and immobilized conductive filaments in HGHS, respectively. Moreover, the continuous potentiation/depression behavior makes the memristor serve as a synapse, featuring broad‐frequency response (10−1–105 Hz, covering 106 frequency range) and multiple‐mode learning (enhanced, depressed, and random‐level modes) based on its natural and motivated forgetting behaviors. Such HGHS‐based memristor also shows good uniformity for 5 × 7 device arrays. Our work paves a way to achieve high‐performance integrated memristors for future artificial neuromorphic computation.https://doi.org/10.1002/inf2.12351artificial synapsesbroad‐frequency rangegradient heterostructureshomologousmemristorsneuromorphic computation |
| spellingShingle | Changjiu Teng Qiangmin Yu Yujie Sun Baofu Ding Wenjun Chen Zehao Zhang Bilu Liu Hui‐Ming Cheng Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation InfoMat artificial synapses broad‐frequency range gradient heterostructures homologous memristors neuromorphic computation |
| title | Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation |
| title_full | Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation |
| title_fullStr | Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation |
| title_full_unstemmed | Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation |
| title_short | Homologous gradient heterostructure‐based artificial synapses for neuromorphic computation |
| title_sort | homologous gradient heterostructure based artificial synapses for neuromorphic computation |
| topic | artificial synapses broad‐frequency range gradient heterostructures homologous memristors neuromorphic computation |
| url | https://doi.org/10.1002/inf2.12351 |
| work_keys_str_mv | AT changjiuteng homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT qiangminyu homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT yujiesun homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT baofuding homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT wenjunchen homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT zehaozhang homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT biluliu homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation AT huimingcheng homologousgradientheterostructurebasedartificialsynapsesforneuromorphiccomputation |