https://scholars.lib.ntu.edu.tw/handle/123456789/637390
標題: | Stretchable photosynaptic transistor with an ultralow energy consumption conferred using conjugated block copolymers/perovskite quantum dots nanocomposites | 作者: | Chen, Wei Cheng Lin, Yan Cheng Hung, Chih Chien Hsu, Li Che Wu, Ya Shuan CHENG-LIANG LIU Kuo, Chi Ching WEN-CHANG CHEN |
關鍵字: | Block copolymers | Perovskite quantum dots | Photosynaptic device | Self-aggregation | Soft electronic | 公開日期: | 1-一月-2023 | 來源出版物: | Materials Today | 摘要: | Emulation of photonic synapses offers a promising avenue for developing low-energy consumption of soft electronics, neurologically inspired robotics, and neuromorphic network computation. In this paper, a fully stretchable photosynaptic device with ultralow energy consumption using intrinsically stretchable poly(δ-decanolactone) (PDL)-based conjugated block copolymers (BCPs) with perovskite quantum dots (PeQDs) is first reported. The findings reveal that selectively choosing solvents for the PDL-based BCPs effectively regulates the assembly of P3HT and the accommodation of PeQDs, leading to improved self-aggregation of PeQDs, increased grain size, and optimized interfaces between P3HT and PeQDs The BCPs/PeQDs composite effectively emulates significant features of photonic synapses, such as paired-pulse facilitation (PPF), spike-dependent and short/long-term neuroplasticity, demonstrating excellent performance, including the fastest response time (1 ms), the highest current contrast (4.9 × 105), PPF (1.93) and ultra-low energy consumption (0.3 aJ) at an operating voltage of –0.1 mV. Furthermore, the BCP/PeQDs exhibit remarkable neuromuscular synapse characteristics, including high strain and bending tolerance and spike-dependent plasticity, enabling the devices to achieve high classification accuracy in artificial neural network simulations during tensile strain. The accommodation solvent selectivity of BCP/PeQDs suggests a promising strategy for advancing neurologically soft electronics, human-like pattern recognition, and neuromorphic computation. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/637390 | ISSN: | 13697021 | DOI: | 10.1016/j.mattod.2023.10.010 |
顯示於: | 化學工程學系 |
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