![Tailoring memory performance via engineering conjugated bridges in benzo[c][1,2,5]thiadiazole based donor–acceptor small molecules](https://public.readdy.ai/ai/img_res/84d936c0c5dd187c2b9d35f9556f69c9.jpg)
Tailoring memory performance via engineering conjugated bridges in benzo[c][1,2,5]thiadiazole based donor–acceptor small molecules
摘要
Tuning the conjugated bridges between the electron-donor and electron-acceptor moieties plays a crucial role in enhancing the memristive properties of organic materials, yet it is rarely reported. Herein, we designed and synthesized four donor–acceptor (D-A) organic small molecules, namely 4,7-bis(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)benzo[c][1,2,5]thiadiazole (DF-BT), 4,7-bis((4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)ethynyl)benzo[c][1,2,5]thiadiazole (DF-ynl-BT), 4,7-bis(5-(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)thiophen-2-yl)benzo[c][1,2,5]thiadiazole (DF-Th-BT), and 4,7-bis((5-(4-((9H-fluoren-9-ylidene)(phenyl)methyl)phenyl)thiophen-2-yl)ethynyl)benzo[c][1,2,5]thiadiazole (DF-Th-ynl-BT), featuring unique conjugated bridges. These molecules were employed as active layers in resistive random-access memory (RRAM) devices to systematically investigate the influence of conjugation bridges on the electrical parameters. The results revealed that devices based on DF-BT, DF-ynl-BT, and DF-Th-BT exhibited write-once-read-many-times (WORM) characteristics, while the DF-Th-ynl-BT-based device demonstrated stable Flash-type switching behavior. Compared to DF-BT, memory devices utilizing DF-ynl-BT, DF-Th-BT, and DF-Th-ynl-BT, which incorporate additional conjugated bridges, exhibited nonvolatile memory properties with reduced threshold voltages, an improved ON/OFF current ratio, enhanced stability, and better uniformity. These findings demonstrated that tailoring the conjugated bridges in D-A molecules can effectively modulate resistive memory behavior and enhance device performance. Furthermore, the DF-Th-ynl-BT-based device was successfully integrated into logic gate circuits and display functions, highlighting its significant potential for applications in artificial intelligence (AI) neural networks.
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