Synaptic plasticity simulates behavior of low-voltage solution-processed Ag-doped CuI thin film transistors
We demonstrate low-temperature solution-processed Ag-doped copper iodide (Ag-doped CuI) thin film transistors (TFTs) gated by chitosan dielectric for energy-efficient neuromorphic applications. Optimized at 8% Ag doping, the devices achieve a high current on/off ratio of 7.1 × 104, steep subthreshold swing (36.68 mV/dec), threshold voltage of 2.08 V, and saturation field-effect mobility of 2.43 cm2/V s. The electric double layer effect of chitosan enables low operating voltages by leveraging its frequency-dependent specific capacitance. Synaptic functionalities, including short-term plasticity, long-term plasticity, and paired-pulse facilitation (PPF), are emulated through proton migration dynamics in the chitosan dielectric, which induces a hysteresis window due to the slower proton transport rate compared to electron conduction in the channel. Systematic stability evaluations under negative bias stress, laser irradiation, and dynamic switching confirm robust operational reliability. The devices exhibit synaptic programmability, with PPF indices tunable via pulse intervals and amplitudes, mimicking biological learning rules. Combined with solution processability and low-temperature fabrication, these Ag-doped CuI TFTs address critical challenges in scalable neuromorphic hardware by integrating ionic-electronic coupling. This work establishes a materials strategy for brain-inspired computing, offering a pathway toward low-power adaptive electronics for portable biosensing and artificial neural networks.
Authors
- Pengfei Chen (ORCID: https://orcid.org/0009-0008-4487-4021)
- Ruifei Xiang
- Dongsheng Tang (ORCID: https://orcid.org/0000-0003-0804-983X)
- Weichang Zhou (ORCID: https://orcid.org/0000-0003-3140-5951)
- Wei Dou (ORCID: https://orcid.org/0000-0003-4936-9151)
- Sunwen Liu
- Junting Liu
- Yuling Peng
- XU Xiao-dong (ORCID: https://orcid.org/0009-0001-9473-4683)
Institutions
- Hunan Normal University (CN)
Publication Details
- Journal
- Applied Physics Letters
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1063/5.0282986
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hunan Province
- Scientific Research Foundation of Hunan Provincial Education Department