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• W4207027199 abstract "Neuromorphic computation possesses the advantages of self-learning, highly parallel computation, and low energy consumption, and is of great promise to overcome the bottleneck of von Neumann computation. In this work, a series of poly(3-hexylthiophene) (P3HT)-based block copolymers (BCPs) with different coil segments, including polystyrene, poly(2-vinylpyridine) (P2VP), poly(2-vinylnaphthalene), and poly(butyl acrylate), are utilized in photosynaptic transistor to emulate paired-pulse facilitation, spike time/rate-dependent plasticity, short/long-term neuroplasticity, and learning-forgetting-relearning processes. P3HT serves as a carrier transport channel and a photogate, while the insulating coils with electrophilic groups are for charge trapping and preservation. Three main factors are unveiled to govern the properties of these P3HT-based BCPs: i) rigidity of the insulating coil, ii) energy levels between the constituent polymers, and iii) electrophilicity of the insulating coil. Accordingly, P3HT-b-P2VP-based photosynaptic transistor with a sought-after BCP combination demonstrates long-term memory behavior with current contrast up to 105 , short-term memory behavior with high paired-pulse facilitation ratio of 1.38, and an ultralow energy consumption of 0.56 fJ at an operating voltage of -0.0003 V. As far as it is known, this is the first work to utilize conjugated BCPs in an electret-free photosynaptic transistor showing great potential to the artificial intelligence technology." @default.
• W4207027199 created "2022-01-26" @default.
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• W4207027199 date "2022-01-22" @default.
• W4207027199 modified "2023-10-06" @default.
• W4207027199 title "Low‐Energy‐Consumption and Electret‐Free Photosynaptic Transistor Utilizing Poly(3‐hexylthiophene)‐Based Conjugated Block Copolymers" @default.
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• W4207027199 doi "https://doi.org/10.1002/advs.202105190" @default.
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