Bias-controlled multistate spintronics with giant TMR and polarity switch via localized spin states in 2D half-metals.

Jialin Yang, Chuyao Chen, Shengli Zhang, Chen Pan, Bin Cheng, Feng Xu, Xi Chen, Erjun Kan, Haibo Zeng

Abstract read

In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Jialin YangMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.ORCID 0009-0008-2213-695X

Chuyao ChenMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.

Shengli ZhangMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.ORCID 0000-0003-4981-231X

Chen PanInstitute of Interdisciplinary Physical Sciences, School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0000-0003-1739-7254

Bin ChengInstitute of Interdisciplinary Physical Sciences, School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0000-0002-2932-4370

Feng XuMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.ORCID 0000-0001-5802-7925

Xi ChenMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.

Erjun KanMIIT Key Laboratory of Semiconductor Microstructure and Quantum Sensing, School of Physics, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0000-0003-0433-4190

Haibo ZengMIIT Key Laboratory of Advanced Display Materials and Devices, Jiangsu Engineering Research Center for Quantum Dot Display, School of Materials Science and Engineering, Institute of Optoelectronics & Nanomaterials, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.ORCID 0000-0002-0281-3617

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The relentless downscaling of integrated circuit systems imposes critical demands on spintronic technologies, particularly requiring superior storage density and efficient tunability beyond conventional magnetic-field approaches. A notable challenge persists in realizing multistate spintronic devices with electrical control at nanoscale. Here, we propose a breakthrough strategy for achieving bias-controlled giant tunneling magnetoresistance (TMR) for multistate memory applications. Our design uses two-dimensional (2D) half-metals hosting strongly localized spin states (LSSs) at the Fermi level as two ferromagnet layers in magnetic tunnel junctions (MTJs). Multiple memory states emerge from bias-driven alignment/misalignment of LSSs on two sides, generating sudden tunneling pathway changes and distinguishable TMR. First-principles quantum transport simulations on MTJs based on 2D VCl

Identifiers

PMID41313773

DOI10.1126/sciadv.adz1524

PMCPMC12662200

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.