ArticleMaterials (Basel, Switzerland)2025
Enhanced Optoelectronic Synaptic Performance in Sol-Gel Derived Al-Doped ZnO Thin Film Devices.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Examination within a Photonic Memory-Based Framework: Al and In Dual-Doped ZnO Thin Film UV Photosensor Devices.ACS applied materials & interfaces · 2026Article
- Substrate Orientation-Dependent Synaptic Plasticity and Visual Memory in Sol-Gel-Derived ZnO Optoelectronic Devices.Materials (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
We report the fabrication and characterization of Al-doped ZnO (AZO) optoelectronic synaptic devices based on sol-gel-derived thin films with varying Al concentrations (0~4.0 wt%). Structural and optical analyses reveal that moderate Al doping modulates the crystal orientation, optical bandgap, and defect levels of ZnO films. Notably, 2.0 wt% Al doping yields the widest bandgap (3.31 eV), stable PL emission, and uniform deep-level absorption without inducing significant lattice disorder. Synaptic performance, including learning-forgetting dynamics and persistent photoconductivity (PPC), is strongly dependent on Al concentration. The 2.0 wt% AZO device exhibits the lowest forgetting rate and longest memory retention due to optimized trap formation, particularly Al-oxygen vacancy complexes that enhance carrier lifetime. Visual memory simulations using a 3 × 3 pixel array under patterned UV illumination further confirm superior long-term memory (LTM) behavior at 2.0 wt%, with stronger excitatory postsynaptic current (EPSC) retention during repeated stimulation. These results demonstrate that precise doping control via the sol-gel method enables defect engineering in oxide-based neuromorphic devices. Our findings provide an effective strategy for designing low-cost, scalable optoelectronic synapses with tunable memory characteristics suitable for future in-sensor computing and neuromorphic vision systems.
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