Programmable Logic and Reservoir Computing Based on Hydrogenation‐Engineered Spin‐Orbit Torque
Abstract To achieve efficient manipulation of magnetism for next‐generation magnetic memory and spintronic devices, integration of distinct electrical control manners within a single device represents a critical pursuit in spintronics. Here, an effective approach combining voltage‐controlled hydrogenation and current‐induced spin‐orbit torque (SOT) is proposed to manipulate the ferromagnetism in an all‐solid‐state magneto‐ionic device with GdO x /Pd/Pd 65 Co 35 heterostructure. Hydrogenation drives a reversible ferromagnetic‐to‐paramagnetic transition, while the SOT enables switching of magnetization without the external magnetic field. Furthermore, SOT‐driven magnetization switching can be finely tuned by the hydrogenation, leading to regulation of switching amplitude, reversal of switching polarity, and reduction of critical switching current. Through magnetization sign and magnitude engineering via coordinated voltage/current inputs, multiple programmable spin logic gates are demonstrated within a single device. Additionally, the semi‐nonvolatile feature of hydrogenation at room temperature results in a nonlinear evolution of magnetization with time and is utilized to construct a simulated physical reservoir computing system, which achieves exceptional performance in temporal tasks, including high accuracy (98.4%) in speech recognition and low error (root mean square error <0.1) in chaotic prediction. This work offers a promising route toward the integration of memory, logic, and neuromorphic computing within a unified spintronic device.
Authors
- Zhaoliang Liao (ORCID: https://orcid.org/0000-0003-1701-9456)
- Haifeng Bu (ORCID: https://orcid.org/0009-0003-5076-7824)
- Zhengxu Zhu
- Zhijian Shen (ORCID: https://orcid.org/0000-0002-7687-3276)
- Shengchun Shen (ORCID: https://orcid.org/0000-0002-7307-2473)
- Wentao Jin (ORCID: https://orcid.org/0000-0002-8397-952X)
- Yulin Gan (ORCID: https://orcid.org/0000-0003-0486-7026)
- Haiyi Zhang (ORCID: https://orcid.org/0009-0008-3889-697X)
- Yuewei Yin (ORCID: https://orcid.org/0000-0003-0965-4951)
- Fangfang Pei
- Qian Li (ORCID: https://orcid.org/0000-0003-0255-4835)
- Xiaoguang Li (ORCID: https://orcid.org/0000-0003-4016-4483)
- Zeyu Guan
- Yuchen Tu
Institutions
- University of Science and Technology of China (CN)
- Hefei National Center for Physical Sciences at Nanoscale (CN)
- Collaborative Innovation Center of Advanced Microstructures (CN)
- National Synchrotron Radiation Laboratory (CN)
- Nanjing University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-09-18
- DOI
- https://doi.org/10.1002/adfm.202519753
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46