Low-power-consumption and excellent-retention-characteristics carbon nanotube optoelectronic synaptic transistors for flexible artificial visual systems

• Published in: Applied materials today Vol. 38; p. 102234
• Main Authors: Zhang, Dan, Li, Yinxiao, Sui, Nianzi, Li, Min, Shao, Shuangshuang, Li, Jiaqi, Li, Benxiang, Yang, Wenming, Wang, Xiaowei, Zhang, Ting, Xu, Wanzhen, Zhao, Jianwen
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2024
• Subjects: Flexible optoelectronic synaptic devices; Image recognitions; Low power consumption; Pyridine-based polyfluorene derivatives; Single-walled carbon nanotubes; Pyridine-based polyfluorene derivatives; Image recognitions; Single-walled carbon nanotubes; Low power consumption; Flexible optoelectronic synaptic devices
• ISSN: 2352-9407; 2352-9415
• DOI: 10.1016/j.apmt.2024.102234

•Developing high-performance optoelectronic synaptic transistor devices only using photosensitive polmyers sorted sc-SWCNTs.•Obtaining low-power-consumption (98.71 aJ) and excellent-memory-characteristics (600 s) flexible SWCNT optoelectronic synaptic TFT devices.•Flexible carbon nanotube artificial visual systems with the recognition accuracy up to 97.06 %.•Greatly decreasing the manufacturing difficulty and cost of SWCNT optoelectronic synaptic devices. Low-power-consumption and excellent-retention-characteristics flexible optoelectronic synaptic devices have become the key units in the advancement of neuromorphic computing systems. In this work, we firstly utilized three photosensitive pyridine-based polyfluorene derivatives to selectively isolate semiconducting single-walled carbon nanotubes (sc-SWCNTs) from commercial SWCNTs and successfully constructed low-power-consumption (98.71 aJ) and excellent-memory-characteristics (Up to 1100s) optoelectronic synaptic SWCNT TFT devices for flexible artificial visual systems (The recognition accuracy up to 97.06 %) without adding any other photosensitive materials in SWCNT TFTs. As-prepared optoelectronic synaptic TFT devices showcase excellent electrical properties with exceptional uniformity, enhancement-mode and high on-off ratios (Up to 106), low operating voltages (-2 V to 0 V), and small subthreshold swings (SS, 75 mV/dec). More importantly, they can simulate not only excitatory postsynaptic currents (EPSCs) and paired-pulse facilitation (PPF, up to 272 %) with the power consumption as low as 98.71 aJ per optical spike under light-pulse stimulation but also the traditional Pavlovian conditioned reflex and artificial visual memory system with excellent memory behaviors (Up to 1100s). Through an in-depth analysis of their working mechanism, we successfully emulated long-term potentiation (LTP) and long-term depression (LTD) phenomena, achieving a 97.06 % accuracy rate in the MNIST (Modified National Institute of Standards and Technology database) recognition task. Furthermore, employing these TFTs, we successfully constructed a five-layer convolutional neural network that operates without any external storage and computational units, validating its image recognition capabilities on the Fashion-MNIST dataset with an accuracy rate of 90.58 %, closely approaching the ideal scenario of 91.25 %. These findings provide a robust technological foundation for the development of highly efficient and flexible artificial visual systems in the future. Here, we firstly utilized photosensitive pyridine-based polyfluorene derivatives to selectively isolate semiconducting single-walled carbon nanotubes from commercial carbon nanotubes and successfully constructed low-power-consumption (98.71 aJ per light spike) and excellent-memory-characteristics (Up to 600 s) optoelectronic synaptic carbon nanotube transistor devices for flexible artificial visual systems (The recognition accuracy up to 97.06 %). Notably, it is no necessary to introduce any additional photosensitive materials in carbon nanotube transistors, and it is the great decreases of the manufacturing difficulty and cost. [Display omitted]