Bare-Die Antiferromagnetic Computing
Abstract
Semiconductor electronic devices are increasingly constrained by fundamental quantum tunneling effects and charge-based mechanisms, which severely limit further miniaturization, write-speed scaling, and environmental robustness of silicon-based technologies. These limitations are particularly prohibitive for deep-space exploration, where extreme temperatures, ultra-strong magnetic fields, and intense radiation rapidly incapacitate conventional electronics without massive shielding. Here, we present an intrinsically resilient, strain-mediated antiferromagnetic MnIr/PMN-PT edge processor that operates reliably as a bare die under temperatures up to 500 K, magnetic fields of 55 T, and radiation doses of 1.5 Mrad. By exploiting an input-modulated in situ self-refreshing encoding mechanism, the device performs nonlinear feature extraction and classification directly from raw analog signals, enabling an analog computing architecture that requires no time-frequency transformation. This architecture achieves 99.8% accuracy in speech recognition without digital preprocessing and 100% accuracy in astronaut visual object recognition. Furthermore, an all-hardware integrated drone vision system demonstrates real-time in situ command execution and autonomous navigation, delivering a terahertz-level response frequency and an ultra-low energy consumption of approximately 0.2 fJ per operation. This work expands the functional scope of antiferromagnetic devices beyond memory and logic, establishing them as a promising materials platform for energy-efficient physical computing and autonomous intelligence in extreme environments.
Versions
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BibTeX
@misc{liu2026baredie,
title = {{Bare-Die Antiferromagnetic Computing}},
author = {Yu Liu and Zhuoting Han and Zexin Feng and Peixin Qin and Zhiyuan Duan and Yuhao Ye and Zengwei Zhu and Chengyan Zhong and Li Liu and Guojian Zhao and Wenbin Shen and Jingyu Li and Sixu Jiang and Xiaoyang Tan and Xiaoning Wang and Ziang Meng and Chengbao Jiang and Zhiqi Liu},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.33460},
eprint = {2609.33460},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.33460},
note = {CC0},
}
RIS
TY - UNPB
TI - Bare-Die Antiferromagnetic Computing
AU - Yu Liu
AU - Zhuoting Han
AU - Zexin Feng
AU - Peixin Qin
AU - Zhiyuan Duan
AU - Yuhao Ye
AU - Zengwei Zhu
AU - Chengyan Zhong
AU - Li Liu
AU - Guojian Zhao
AU - Wenbin Shen
AU - Jingyu Li
AU - Sixu Jiang
AU - Xiaoyang Tan
AU - Xiaoning Wang
AU - Ziang Meng
AU - Chengbao Jiang
AU - Zhiqi Liu
PY - 2026
DA - 2026/09/27
PB - arXiv
DO - 10.48550/arxiv.2609.33460
UR - https://arxiv.org/abs/2609.33460
AB - Semiconductor electronic devices are increasingly constrained by fundamental quantum tunneling effects and charge-based mechanisms, which severely limit further miniaturization, write-speed scaling, and environmental robustness of silicon-based technologies. These limitations are particularly prohibitive for deep-space exploration, where extreme temperatures, ultra-strong magnetic fields, and intense radiation rapidly incapacitate conventional electronics without massive shielding. Here, we present an intrinsically resilient, strain-mediated antiferromagnetic MnIr/PMN-PT edge processor that operates reliably as a bare die under temperatures up to 500 K, magnetic fields of 55 T, and radiation doses of 1.5 Mrad. By exploiting an input-modulated in situ self-refreshing encoding mechanism, the device performs nonlinear feature extraction and classification directly from raw analog signals, enabling an analog computing architecture that requires no time-frequency transformation. This architecture achieves 99.8% accuracy in speech recognition without digital preprocessing and 100% accuracy in astronaut visual object recognition. Furthermore, an all-hardware integrated drone vision system demonstrates real-time in situ command execution and autonomous navigation, delivering a terahertz-level response frequency and an ultra-low energy consumption of approximately 0.2 fJ per operation. This work expands the functional scope of antiferromagnetic devices beyond memory and logic, establishing them as a promising materials platform for energy-efficient physical computing and autonomous intelligence in extreme environments.
ER -