Chloride Modulation Doping in Dual-Channel InGaZnO Synaptic Transistors: Location-Selective Strategy for Synaptic Plasticity Enhancement
Chloride ion modulation-doped InGaZnO dual-channel synaptic transistors (DCSTs) were fabricated via mist chemical vapor deposition to evaluate the role of location-selective doping on synaptic plasticity. DCST with a Cl– doped front channel (MOD-DCST (F)) significantly enhanced long-term plasticity, resulting in superior synaptic weight retention, improved conductance linearity, and reliable LTP/LTD characteristics. In contrast, DCST with a Cl– doped back channel (MOD-DCST (B)) primarily reinforced short-term plasticity, achieving high paired-pulse facilitation (PPF) index of 170% and spike-rate dependent plasticity (SRDP) index of 288% which are essential for temporal computation tasks such as high-pass filtering, real-time information processing and sound-source localization. DCST with Cl doping in both channels (DCST (F&B)) led to partial cancellation of these effects. The devices reproduced essential synaptic behaviors such as EPSC, PPF, SNDP, SRDP, and LTP/LTD. When applied in a CNN model with weight quantization, MOD-DCST (F) achieved the highest recognition accuracy of 94.95% on the MNIST data set, outperforming the 88.78% of undoped DCST and 77.12% of MOD-DCST (B). These results demonstrate that location-selective chloride modulation doping enables application-oriented tunability: front-channel doping favors memory-centric learning, whereas back-channel doping supports temporal information processing, establishing a versatile platform for multifunctional neuromorphic computing.
Authors
- Han-Yin Liu (ORCID: https://orcid.org/0000-0003-1150-7192)
- Junyi Wu (ORCID: https://orcid.org/0000-0002-2509-1223)
- Zehao Lin (ORCID: https://orcid.org/0000-0002-5308-1065)
- Ying Chao (ORCID: https://orcid.org/0000-0001-7129-3435)
- Song ChengYi
- Jie-Fei Jiang
- Han-Wei Chen (ORCID: https://orcid.org/0009-0006-7892-8660)
Institutions
- National Sun Yat-sen University (TW)
Publication Details
- Journal
- ACS Applied Electronic Materials
- Published
- 2025-12-05
- DOI
- https://doi.org/10.1021/acsaelm.5c02076
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00