@article{Wang2026, 
author = {Zining Wang and Chensi Song and Huanyu Chen and Xinsheng Liu and Xi Zhang and Jichun Zhu and Huilin Li},
title = {Robust Sb2Se3 memristors via pressure-modulated growth for noise-resilient neuromorphic computing},
year = {2026},
journal = {Nano Research},
keywords = {neuromorphic computing, memristors, edge intelligence, antimony selenide, rapid thermal evaporation, noise-resilient},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908881},
doi = {10.26599/NR.2026.94908881},
abstract = {High-fidelity neuromorphic computing requires synaptic hardware that balances analog precision with array-level noise immunity, yet suppressing leakage currents in chalcogenide crossbars often necessitates complex interface engineering. Here, a robust Ag/Sb2Se3/ITO synapse is reported, fabricated via a pressure-modulated rapid thermal evaporation (RTE) strategy that targets intrinsic defect control. Crucially, this thermodynamic optimization preserves the ultralow intrinsic carrier concentration (~1014 cm-3) of the Sb2Se3 functional layer, physically prohibiting background leakage pathways without requiring additional buffer layers. Consequently, the device demonstrates highly uniform analog switching behavior, a substantial ON/OFF ratio (>105), and low set/reset voltages. These characteristics effectively suppress sneak path currents and maximize the sensing margins within the crossbar array. At the system level, physics-based neural networks achieve 96.3% accuracy on MNIST, maintaining exceptional robustness against severe salt-and-pepper noise. Furthermore, we demonstrate that the hardware can execute complex motion perception algorithms, successfully extracting clear motion edges in dynamic spatiotemporal scenarios. This work establishes intrinsic carrier concentration modulation as a minimalist yet powerful paradigm for next-generation noise-resilient edge intelligence.}
}