Cargando…

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

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 n...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Bin, Chen, Weilin, Zeng, Jianmin, Fan, Fei, Gu, Junwei, Chen, Xinhui, Yan, Lin, Xie, Guangjun, Liu, Shuzhi, Yan, Qing, Baik, Seung Jae, Zhang, Zhi-Guo, Chen, Weihua, Hou, Jie, El-Khouly, Mohamed E., Zhang, Zhang, Liu, Gang, Chen, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012610/
https://www.ncbi.nlm.nih.gov/pubmed/33790277
http://dx.doi.org/10.1038/s41467-021-22243-8
_version_ 1783673400037736448
author Zhang, Bin
Chen, Weilin
Zeng, Jianmin
Fan, Fei
Gu, Junwei
Chen, Xinhui
Yan, Lin
Xie, Guangjun
Liu, Shuzhi
Yan, Qing
Baik, Seung Jae
Zhang, Zhi-Guo
Chen, Weihua
Hou, Jie
El-Khouly, Mohamed E.
Zhang, Zhang
Liu, Gang
Chen, Yu
author_facet Zhang, Bin
Chen, Weilin
Zeng, Jianmin
Fan, Fei
Gu, Junwei
Chen, Xinhui
Yan, Lin
Xie, Guangjun
Liu, Shuzhi
Yan, Qing
Baik, Seung Jae
Zhang, Zhi-Guo
Chen, Weihua
Hou, Jie
El-Khouly, Mohamed E.
Zhang, Zhang
Liu, Gang
Chen, Yu
author_sort Zhang, Bin
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8012610
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-80126102021-04-16 90% yield production of polymer nano-memristor for in-memory computing Zhang, Bin Chen, Weilin Zeng, Jianmin Fan, Fei Gu, Junwei Chen, Xinhui Yan, Lin Xie, Guangjun Liu, Shuzhi Yan, Qing Baik, Seung Jae Zhang, Zhi-Guo Chen, Weihua Hou, Jie El-Khouly, Mohamed E. Zhang, Zhang Liu, Gang Chen, Yu Nat Commun Article 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. Nature Publishing Group UK 2021-03-31 /pmc/articles/PMC8012610/ /pubmed/33790277 http://dx.doi.org/10.1038/s41467-021-22243-8 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Zhang, Bin
Chen, Weilin
Zeng, Jianmin
Fan, Fei
Gu, Junwei
Chen, Xinhui
Yan, Lin
Xie, Guangjun
Liu, Shuzhi
Yan, Qing
Baik, Seung Jae
Zhang, Zhi-Guo
Chen, Weihua
Hou, Jie
El-Khouly, Mohamed E.
Zhang, Zhang
Liu, Gang
Chen, Yu
90% yield production of polymer nano-memristor for in-memory computing
title 90% yield production of polymer nano-memristor for in-memory computing
title_full 90% yield production of polymer nano-memristor for in-memory computing
title_fullStr 90% yield production of polymer nano-memristor for in-memory computing
title_full_unstemmed 90% yield production of polymer nano-memristor for in-memory computing
title_short 90% yield production of polymer nano-memristor for in-memory computing
title_sort 90% yield production of polymer nano-memristor for in-memory computing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8012610/
https://www.ncbi.nlm.nih.gov/pubmed/33790277
http://dx.doi.org/10.1038/s41467-021-22243-8
work_keys_str_mv AT zhangbin 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT chenweilin 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT zengjianmin 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT fanfei 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT gujunwei 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT chenxinhui 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT yanlin 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT xieguangjun 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT liushuzhi 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT yanqing 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT baikseungjae 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT zhangzhiguo 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT chenweihua 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT houjie 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT elkhoulymohamede 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT zhangzhang 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT liugang 90yieldproductionofpolymernanomemristorforinmemorycomputing
AT chenyu 90yieldproductionofpolymernanomemristorforinmemorycomputing