Evidence map›Paper›PMID 41450222›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Three-Dimensional Heterogeneous Bonding for High-Density and Low-Noise TMR Sensing Arrays.

Zi'ang Han, Zhenhu Jin, Chenglong Zhang, Jiamin Chen

Erratum issuedAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Zi'ang HanState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Zhenhu JinState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Chenglong ZhangState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.
Jiamin ChenState Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China.ORCID https://orcid.org/0000-0003-0538-5520

Funding

Beijing Municipal Natural Science Foundation L255006National Key Research and Development Program of China 2023YFB2407800National Natural Science Foundation of China 62271469One Hundred Person Project of the Chinese Academy of SciencesScience and Disruptive Technology Program AIRCAS (2025-AIRCAS-SDTP-02)Xiaomi Young Talents ProgramYoung Elite Scientists Sponsorship Program YESS20210341
6 · The paper itself

Abstract

Tunneling magnetoresistance (TMR) devices are crucial for high-density low-power magnetic sensing owing to the increase in data-intensive applications. This study presents a three-dimensional heterogeneous integration bonding technique for TMR sensors. The number of serially connected magnetic tunnel junctions is doubled without enlarging the chip area by vertically bonding two TMR film stacks, and the low-frequency 1/f noise is effectively suppressed. The sputtering parameters of the Cr/Au bonding layer, argon-ion activation conditions, and bonding pressure are optimized to achieve a void ratio of only 0.73%. Double-layer TMR devices are fabricated via backside silicon removal, photolithography, and ion-beam etching. A 45° etching angle mitigates sidewall redeposition, increases the magnetoresistance ratio from 149% to 172%, and decreases the magnetic noise to 0.97 nT·Hz

Indexed as

high‐density magnetic sensing arrayslow‐frequency 1/f noise suppressionmagnetoresistance enhancementthree‐dimensional heterogeneous integration bondingtunneling magnetoresistance sensorswafer‐level bonding and etching optimization

Identifiers

PMID41450222
PMCPMC12955946

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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.