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Published on: June 23, 2018
Polyzwitterion with Imidazole for Ultralow-Power Organic Photoelectric Synaptic Transistors Achieving Multicolor
Jingpeng Wu1, Yi Zhao1, Chaoxiong Guo1
1Frontier Institute of Science and Technology, State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, China.
Abstract:
Neuromorphic in-sensor vision demands photoelectric synaptic transistors that co-localize optical sensing and electrically tunable plasticity in a compact device, thereby simplifying artificial visual sensing circuits. Electrolytes and ionic liquids are widely employed as dielectric layers for neuromorphic transistors; however, cation-anion pairs without covalent bonds typically suffer from ion-migration, ion replacement, and electrochemical reactions. These processes lead to current leakage, elevated power consumption, and irreversible drift, while photo-modulation is further attenuated by exciton-polaron quenching. Here, to stabilize ionic charges and suppress ion-migration, a polyzwitterion, poly(3-(1-vinyl-3-imidazolyl)-propanesulfonate), is used as a single medium to unify electro- and photo-synaptic functions, thereby simplifying the components and interfaces of the multilayer device. The aromatic conjugated imidazole ring stabilizes the cationic charge, and the covalently tethered cation-anion pair prevents long-distance ion-migration, ensuring strong cation-anion dipole polarization and reversible charge trapping. This enables robust multi-wavelength photo-synaptic modulation coupled with electro-synaptic plasticity. The resulting organic photoelectric synaptic transistors achieve ultralow event energy consumption down to 10.95 aJ and support application-relevant in-sensor demonstrations, including encrypted image decoding, color-guided robotic manipulation, pedestrian-speed recognition, and motion prediction. These findings are poised to accelerate the development of multifunctional photoelectric synaptic devices and integrated sensing-computing hardware architectures for artificial visual systems.
