Printed Optoelectronic Memories Using Gr/WS<sub>2</sub> Nanostructured Composite Ink for Retina‐Inspired Vision Persistent Synapses

Abstract The rapid advancement of neuromorphic computing and machine vision drives the need for optoelectronic memories that mimic neural and visual systems, integrating optical sensing, data storage, and processing. Traditional fabrication methods are often complex, multistep processes that struggle to achieve lightweight, scalable, and flexible designs. This limitation highlights the need for alternative approaches like printing technologies to enable flexible optoelectronic memory development. Here, a novel approach is presented to print optoelectronic memories using graphene (Gr)/WS 2 nanostructured composite ink. This composite ink utilizes Gr nanosheets as conductive channels and defect sites in WS 2 as charge capture centers, forming local heterojunctions that enable efficient photoelectric storage. Two types of Gr/WS 2 composite inks are developed, tested, and compared with pure Gr ink. The findings reveal that the Gr/WS 2 nanocomposite ink with enhanced edge states exhibits superior memory performance. Devices print using this ink demonstrated the ability to store visual information in both single‐pulse and multi‐pulse modes, reflecting potential applications in retina‐inspired visual persistence and neuromorphic computing. This work highlights the promise of printed 2D material‐based optoelectronic memories for advancing scalable, low‐cost, and flexible electronic devices.

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Publication Details

Journal
Advanced Electronic Materials
Published
2025-01-27
DOI
https://doi.org/10.1002/aelm.202400760
Citations
2
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.46

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article

Printed Optoelectronic Memories Using Gr/WS2 Nanostructured Composite Ink for Retina‐Inspired Vision Persistent Synapses

Cheng‐Di Dong, Renjing Xu, Hongbing Zhan, Jiahui Bai et al.
2 citations
Advanced Electronic Materials
Advanced Memory and Neural Computing
1.46
article

Printed Optoelectronic Memories Using Gr/WS2 Nanostructured Composite Ink for Retina‐Inspired Vision Persistent Synapses

Cheng‐Di Dong, Renjing Xu, Hongbing Zhan, Jiahui Bai, Jiajie Pei, Qiaoqiao Zheng, Qiuyan Wang
article en
2 citations

Abstract

Abstract The rapid advancement of neuromorphic computing and machine vision drives the need for optoelectronic memories that mimic neural and visual systems, integrating optical sensing, data storage, and processing. Traditional fabrication methods are often complex, multistep processes that struggle to achieve lightweight, scalable, and flexible designs. This limitation highlights the need for alternative approaches like printing technologies to enable flexible optoelectronic memory development. Here, a novel approach is presented to print optoelectronic memories using graphene (Gr)/WS 2 nanostructured composite ink. This composite ink utilizes Gr nanosheets as conductive channels and defect sites in WS 2 as charge capture centers, forming local heterojunctions that enable efficient photoelectric storage. Two types of Gr/WS 2 composite inks are developed, tested, and compared with pure Gr ink. The findings reveal that the Gr/WS 2 nanocomposite ink with enhanced edge states exhibits superior memory performance. Devices print using this ink demonstrated the ability to store visual information in both single‐pulse and multi‐pulse modes, reflecting potential applications in retina‐inspired visual persistence and neuromorphic computing. This work highlights the promise of printed 2D material‐based optoelectronic memories for advancing scalable, low‐cost, and flexible electronic devices.

Advanced Electronic MaterialsVol. 11(8)
Hong Kong University of Science and Technology (HK), Minjiang University (CN), Fuzhou University (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Natural Science Foundation of Shandong Province, Minjiang University
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
1.46
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