Symmetric Azobenzene‐Type Small Molecules With Tunable Rigidity for High Performance Nonvolatile Organic Memory Devices

ABSTRACT The rational design of functional molecular semiconductors is essential for advancing high‐performance organic memory technologies. Here, we report two symmetric donor–acceptor (D–A) azobenzene‐type small molecules, Cz‐Azo and DPA‐Azo, featuring tunable molecular rigidity and terminal electron‐donating substituents. Both molecules, as active layers in organic resistive memory devices, exhibit reliable nonvolatile bipolar resistive memory behavior with high ON/OFF current ratios, excellent cycling stability, and low operational voltages. Detailed characterizations reveal highly crystalline and densely packed molecular structures of Cz‐Azo and DPA‐Azo films, while theoretical calculations confirm that the rigid molecular backbone provides continuous channels for charge transfer (CT) and prevents conformational barriers in the excited state, leading to stable and reversible switching behaviors. Beyond their robust memory characteristics, the devices enable logic operations and exhibit neuromorphic computing potential, as demonstrated by the Cz‐Azo‐based device achieving 98.14% handwritten digit recognition accuracy on the Modified National Institute of Standards and Technology database (MNIST) dataset using a simulated multilayer perceptron. This study highlights molecular rigidity engineering and D‐A modulation in azobenzene‐based small molecules as an effective strategy for optimizing charge transport, storage stability, and device performance, providing new insights for the development of next‐generation high‐performance organic memory materials.

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Publication Details

Journal
Advanced Materials Technologies
Published
2026-06-26
DOI
https://doi.org/10.1002/admt.71123
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Symmetric Azobenzene‐Type Small Molecules With Tunable Rigidity for High Performance Nonvolatile Organic Memory Devices

Hong Lian, Shuanglong Wang, Qingchen Dong, Xi Zhang et al.
Advanced Materials Technologies
Advanced Memory and Neural Computing
article

Symmetric Azobenzene‐Type Small Molecules With Tunable Rigidity for High Performance Nonvolatile Organic Memory Devices

Hong Lian, Shuanglong Wang, Qingchen Dong, Xi Zhang, Liu Y, Lingling Yao, Jiangnan Xia, Zhaoxin Xu, Jiahui Ding, Shihui Dong
article en

Abstract

ABSTRACT The rational design of functional molecular semiconductors is essential for advancing high‐performance organic memory technologies. Here, we report two symmetric donor–acceptor (D–A) azobenzene‐type small molecules, Cz‐Azo and DPA‐Azo, featuring tunable molecular rigidity and terminal electron‐donating substituents. Both molecules, as active layers in organic resistive memory devices, exhibit reliable nonvolatile bipolar resistive memory behavior with high ON/OFF current ratios, excellent cycling stability, and low operational voltages. Detailed characterizations reveal highly crystalline and densely packed molecular structures of Cz‐Azo and DPA‐Azo films, while theoretical calculations confirm that the rigid molecular backbone provides continuous channels for charge transfer (CT) and prevents conformational barriers in the excited state, leading to stable and reversible switching behaviors. Beyond their robust memory characteristics, the devices enable logic operations and exhibit neuromorphic computing potential, as demonstrated by the Cz‐Azo‐based device achieving 98.14% handwritten digit recognition accuracy on the Modified National Institute of Standards and Technology database (MNIST) dataset using a simulated multilayer perceptron. This study highlights molecular rigidity engineering and D‐A modulation in azobenzene‐based small molecules as an effective strategy for optimizing charge transport, storage stability, and device performance, providing new insights for the development of next‐generation high‐performance organic memory materials.

Advanced Materials Technologies
Shanghai University (CN), Shanghai University of Engineering Science (CN), Hong Kong Polytechnic University (HK), Taiyuan University of Technology (CN)
National Natural Science Foundation of China, Shanghai University
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
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