@article{Song2026, 
author = {Jeong Hwan Song and Changmin Lim and Jae-Seung Jeong and Shinyeong Park and Min Kyu Yang and Jiwon Chang and Gun Hwan Kim},
title = {Carbon-doped GeTe-based ovonic threshold switch for highly reliable artificial neuron devices},
year = {2026},
journal = {International Journal of Extreme Manufacturing},
volume = {8},
number = {3},
keywords = {carbon-doped germanium telluride, ovonic threshold switching neuron devices, threshold and holding voltage differences, trap depth variations, machine-learning molecular dynamics simulations, tempotron learning rules, neuromorphic computing hardware},
url = {https://www.sciopen.com/article/10.1088/2631-7990/ae37ae},
doi = {10.1088/2631-7990/ae37ae},
abstract = {Spiking neural networks (SNNs) rely on precise spike timing for computation; however, their performance often suffers jitter-induced errors and constraints on synaptic weight updates. In this study, we address this challenge by expanding the neuronal integration window, enhancing temporal robustness while maintaining efficient learning dynamics. We introduce a material-driven approach to expand the operational window of artificial neuron devices, which is defined as the difference between the threshold voltage (Vth) and holding voltage (Vhold), in ovonic threshold switch (OTS)-based neurons, demonstrating its direct impact on synaptic weight updates and error mitigation. Carbon doping in GeTe-based OTS devices is employed to systematically modulate trap depth variations under an electric field, achieving precise control over Vth and Vhold. Electrical measurements confirm that an optimal 3.4% carbon concentration maximizes the operational window, stabilizing threshold switching and ensuring reliable neuronal operation. To reveal the atomic-scale mechanisms behind this behavior, we perform density functional theory (DFT) simulations, analyzing coordination number and bond angles to elucidate how carbon incorporation modifies trap distributions and influences device characteristics. Finally, we assess the practical impact of operational window expansion by implementing the optimized OTS neurons in a learning framework based on the tempotron learning rule, revealing enhanced spike timing robustness and reduced synaptic weight update constraints. This study provides a scalable pathway toward more reliable spike-based neuromorphic computing to advance the next generation of artificial intelligence hardware.}
}