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Engineering Conjugated Bridges in TPE-BT-Based Donor-Acceptor Molecules for Optimized Resistive Random Access Memory
Xinwei Liu1, Hong Lian2, Liang Zhao3
1School of Mechanical & Electronic Engineering and Automation, Shanghai University, No.99 Shangda Road, Baoshan District, Shanghai 200444, China.
Abstract:
Four donor-acceptor (D-A) type organic small molecules, namely, 4,7-bis(4-(1,2,2-triphenylvinyl)phenyl)benzo[c][1,2,5]thiadiazole(TPE-BT), 4,7-bis((4-(1,2,2-triphenylvinyl)phenyl)ethynyl)benzo[c][1,2,5]thiadiazole(TPE-ynl-BT), 4,7-bis(5-(4-(1,2,2-triphenylvinyl)phenyl)thiophen-2-yl)benzo[c][1,2,5]thiadiazole (TPE-Th-BT), and 4,7-bis((5-(4-(1,2,2-triphenylvinyl)phenyl)thiophen-2yl)ethynyl)benzo[c][1,2,5]thiadiazole(TPE-Th-ynl-BT), each incorporating unique conjugated bridges, are designed, synthesized, and integrated into resistive random access memory (RRAM) devices. Current-voltage (I-V) measurements indicate that the TPE-BT, TPE-ynl-BT and TPE-Th-BT based devices exhibit write-once-read-many-times (WORM) characteristics, while TPE-Th-ynl-BT based devices show a stable flash-type switching behavior. In comparison to TPE-BT, the memory devices constructed with TPE-ynl-BT, TPE-Th-BT and TPE-Th-ynl-BT, which include additional conjugated bridges, exhibit nonvolatile memory capabilities with reduced threshold voltages, higher ION/IOFF (104:1), 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-Th-ynl-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.
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