Статья

90% yield production of polymer nano-memristor for in-memory computing

Bin ZhangKey Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, ChinaWeilin ChenGreen Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou, ChinaJianmin ZengSchool of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, ChinaFei FanKey Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, ChinaJunwei GuShaanxi Key Laboratory of Macromolecular Science and Technology, School of Chemistry and Chemical Engineering, Northwestern Polytechnical University, Xi' an, Shaanxi, ChinaXinhui ChenSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaLin YanSchool of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, ChinaGuangjun XieSchool of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, ChinaShuzhi LiuSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaQing YanKey Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, ChinaSeung Jae BaikSchool of Electronic and Electrical Engineering, Hankyong National University, Anseong-si, Gyeonggi-do, KoreaZhiguo ZhangGreen Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou, ChinaWeihua ChenGreen Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou, ChinaJie HouKey Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, ChinaMohamed E. El‐KhoulyInstitude of Basic and Applied Sciences, Egypt-Japan University of Science and Technology (E-JUST), Alexandria, EgyptZhang ZhangGreen Catalysis Center and College of Chemistry, Zhengzhou University, Zhengzhou, ChinaGang LiuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China. [email protected]Yu ChenKey Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China. [email protected]

2021en

ABI

Аннотация

Abstract Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π – π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10 −15 J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks.

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Идентификаторы

DOI: 10.1038/s41467-021-22243-8

Цитирования и источники

Цитирований: 2Использованных источников: 0

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