UPDF AI

Low-voltage transistor array with dynamic synaptic plasticity for neuromorphic computing

Pengfei Chen,Wei Dou,5 作者,Dongsheng Tang

2025 · DOI: 10.1063/5.0273488
Applied Physics Letters · 引用数 0

TLDR

The integrated junctionless indium-tin-oxide transistor arrays for neuromorphic computing are demonstrated, exhibiting remarkable stability and consistency, and a feedback-enhanced learning model was constructed to emulate spatiotemporal integration of neural signals.

摘要

This study demonstrates the integrated junctionless indium-tin-oxide transistor arrays for neuromorphic computing, exhibiting remarkable stability and consistency. Stable and dynamic synaptic plasticity is demonstrated by these devices, with both paired pulse facilitation (PPF) and paired pulse depression (PPD) being achieved within a single device. Through the modulation of synaptic weights, the dynamic conversion from PPD to PPF can be realized, thereby enabling multimodal learning and reducing the complexity of the neuromorphic system. The unique ion migration mechanism of chitosan electrolytes enables short-term plasticity and pulse-number-dependent weight modulation. Utilizing these properties, a feedback-enhanced learning model was constructed to emulate spatiotemporal integration of neural signals. The array exhibits excellent scalability, offering a cost-effective solution for large-scale neuromorphic systems. Notably, the controllable switch between inhibition and potentiation modes represents a demonstrated capability in artificial synapse design, holding promise for bioelectronic devices and adaptive sensing applications.

参考文献
引用文献