Tailoring memory performance via engineering conjugated bridges in benzo[c][1,2,5]thiadiazole based donor–acceptor small molecules

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. Engineered by integrating diverse conjugated bridges (- ynl , - Th , and - Th-ynl ) into the donor-acceptor (D-A) molecular backbone, this study presents a high-performance organic resistive random-access memory (RRAM). Notably, the DF- Th-ynl -BT device exhibits remarkable potential for application in artificial intelligence networks. This is validated by its successful implementation in logic gate operations and ASCII Art functions. Its efficient performance and versatility make it a promising candidate for advancing AI technologies.