{"doi":"10.1002/admt.202401797","title":"Low‐Noise, Unbiased Ferromagnetic‐Resonance‐Driven Thin‐Film Integrated Giant Magnetoimpedance Sensors","abstract":"Abstract Magnetic sensors are vital in modern technology, with extensive applications across various engineering and industrial fields. The Giant Magnetoimpedance (GMI) effect offers advantages such as high magnetic field sensitivity, spatial resolution, and low power consumption, making it a key focus for ultrasensitive magnetic sensors. This paper presents an unbiased, low‐noise GMI sensor based on FMR‐driven S 21 phase change in a Ni 81 Fe 19 /Ti/Cu/Ni 81 Fe 19 /Ti multilayer microstrip. Fabricated using photolithography and sputtering on a silicon wafer, the sensor exhibits favorable magnetic properties, including low coercivity, narrow ferromagnetic resonance linewidth, and well‐defined magnetic domain walls along the microstrip width direction (easy axis). Phase noise and magnetic noise are directly measured using a phase noise setup, without electronic conditioning circuits. The study examines phase sensitivity, phase noise, and magnetic noise as functions of RF frequency, DC bias field, and input power, achieving the best equivalent magnetic noise of ≈100 pT/√Hz at 15 Hz at zero bias field. The optimal RF frequency is 700 MHz with an input power of −2 dBm. The compact design, unbiased operation, low magnetic noise, and MEMS‐based fabrication make these integrated GMI sensors promising for fundamental research and industrial applications requiring precise magnetic field detection.","journal":"Advanced Materials Technologies","year":2025,"id":552295,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9501,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":624223,"name":"Xianfeng Liang","orcid":"0000-0003-4055-1985","position":1,"is_corresponding":false},{"id":624224,"name":"Huaihao Chen","orcid":"0000-0003-0253-2089","position":2,"is_corresponding":false},{"id":1448763,"name":"Cai Müller","orcid":null,"position":3,"is_corresponding":false},{"id":1448449,"name":"Paul Raschdorf","orcid":"0009-0003-3095-4438","position":4,"is_corresponding":false},{"id":1448450,"name":"Phillip Durdaut","orcid":"0000-0001-9839-3890","position":5,"is_corresponding":false},{"id":1448451,"name":"Michael Höft","orcid":"0000-0001-9352-2868","position":6,"is_corresponding":false},{"id":719077,"name":"Jeffrey McCord","orcid":"0000-0003-0237-6450","position":7,"is_corresponding":false},{"id":371457,"name":"Nian X. Sun","orcid":"0000-0002-3120-0094","position":8,"is_corresponding":false},{"id":1204090,"name":"Bin Luo","orcid":"0000-0003-2419-8683","position":0,"is_corresponding":true}],"reference_count":76,"raw_metadata":null,"created_at":"2026-07-19T02:54:33.203144Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}