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

Bin Zhang, Weilin Chen, Jianmin Zeng, Fei Fan, Junwei Gu, Xinhui Chen, Lin Yan, Guangjun Xie, Shuzhi Liu, Qing Yan, Seung Jae Baik, Zhi-Guo Zhang, Weihua Chen, Jie Hou, Mohamed E. El-Khouly, Zhang Zhang (), Gang Liu () and Yu Chen ()
Additional contact information
Bin Zhang: East China University of Science and Technology
Weilin Chen: Shanghai Jiao Tong University
Jianmin Zeng: Hefei University of Technology
Fei Fan: East China University of Science and Technology
Junwei Gu: Northwestern Polytechnical University
Xinhui Chen: Shanghai Jiao Tong University
Lin Yan: Hefei University of Technology
Guangjun Xie: Hefei University of Technology
Shuzhi Liu: Shanghai Jiao Tong University
Qing Yan: East China University of Science and Technology
Seung Jae Baik: Hankyong National University
Zhi-Guo Zhang: Zhengzhou University
Weihua Chen: Zhengzhou University
Jie Hou: East China University of Science and Technology
Mohamed E. El-Khouly: Egypt-Japan University of Science and Technology (E-JUST)
Zhang Zhang: Hefei University of Technology
Gang Liu: Shanghai Jiao Tong University
Yu Chen: East China University of Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: 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.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-021-22243-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22243-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-22243-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().