Engineering Conjugated Bridges in TPE-BT-Based Donor–Acceptor Molecules for Optimized Resistive Random Access Memory
Four donor-acceptor (D-A) type organic small molecules, namely, 4,7-bis(4-(1,2,2-triphenylvinyl)phenyl)benzo[<i>c</i>][1,2,5]thiadiazole(TPE-BT), 4,7-bis((4-(1,2,2-triphenylvinyl)phenyl)ethynyl)benzo[<i>c</i>][1,2,5]thiadiazole(TPE-<i>ynl</i>-BT), 4,7-bis(5-(4-(1,2,2-triphenylvinyl)phenyl)thiophen-2-yl)benzo[<i>c</i>][1,2,5]thiadiazole (TPE-<i>Th</i>-BT), and 4,7-bis((5-(4-(1,2,2-triphenylvinyl)phenyl)thiophen-2yl)ethynyl)benzo[<i>c</i>][1,2,5]thiadiazole(TPE-<i>Th</i>-<i>ynl</i>-BT), each incorporating unique conjugated bridges, are designed, synthesized, and integrated into resistive random access memory (RRAM) devices. Current-voltage (<i>I-V</i>) measurements indicate that the TPE-BT, TPE-<i>ynl</i>-BT and TPE-<i>Th</i>-BT based devices exhibit write-once-read-many-times (WORM) characteristics, while TPE<i>-Th-yn</i>l-BT based devices show a stable flash-type switching behavior. In comparison to TPE-BT, the memory devices constructed with TPE-<i>ynl</i>-BT, TPE-<i>Th</i>-BT and TPE-<i>Th</i>-<i>ynl</i>-BT, which include additional conjugated bridges, exhibit nonvolatile memory capabilities with reduced threshold voltages, higher <i>I</i><sub>ON</sub>/<i>I</i><sub>OFF</sub> (10<sup>4</sup>:1), enhanced stability, and improved reproducibility. The photophysical, electrochemical analyses, and X-ray diffraction (XRD) results reveal that incorporating conjugated bridges within molecular structures can enhance data storage performance while reducing power consumption. Our findings demonstrate that these conjugated bridges play a crucial role in optimizing electrical memory characteristics and resistive switching behavior. Moreover, the device fabricated with TPE-<i>Th-ynl</i>-BT is effectively applied to logic gate circuits and American Standard Code for Information Interchange (ASCII) art function, highlighting its promising potential as a smart sensor within artificial intelligence (AI) networks.
Authors
- Hong Lian (ORCID: https://orcid.org/0000-0001-5884-4917)
- Shuanglong Wang (ORCID: https://orcid.org/0000-0002-7016-791X)
- Xinyu Jiang (ORCID: https://orcid.org/0000-0003-2580-9431)
- Tianfu Guan (ORCID: https://orcid.org/0000-0002-9887-9265)
- Qingchen Dong (ORCID: https://orcid.org/0000-0003-4363-2748)
- Tianxiao Xiao (ORCID: https://orcid.org/0000-0002-5013-4010)
- Liang Zhao (ORCID: https://orcid.org/0000-0002-5687-1265)
- Peter Müller‐Buschbaum (ORCID: https://orcid.org/0000-0002-9566-6088)
- Xinwei Liu (ORCID: https://orcid.org/0000-0002-9829-3395)
- Zhitao Qin
Institutions
- Shanghai University (CN)
- Chinese University of Hong Kong (HK)
- Deutsches Elektronen-Synchrotron DESY (DE)
- Technical University of Munich (DE)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1021/acsami.5c03859
- Citations
- 3
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 2.19
Funders
- Deutsche Forschungsgemeinschaft
- National Natural Science Foundation of China