Standby magnetic immunity calculator for STT-MRAM

This work investigates the impact of external magnetic fields, field orientation, temperature, and exposure time on the standby magnetic immunity (SMI) of spin-transfer torque magnetoresistive random-access memory. Wafer-level measurements were performed using a 3D vector magnet, ensuring uniform magnetic fields on unpackaged and unshielded wafers. The analytical model, used to predict the SMI safety field for long-term exposure, has been validated against hardware data and micromagnetic simulations. Furthermore, we introduce a machine-learning-based SMI calculator that provides a fast, automated estimation of the safety field for complex mission profiles. This approach bridges physics-based modeling and data-driven prediction, providing a practical tool for estimating the SMI of MRAM for automotive and other safety-critical environments.

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Publication Details

Journal
AIP Advances
Published
2026-02-01
DOI
https://doi.org/10.1063/9.0001036
Primary Topic
Magnetic Field Sensors Techniques
Type
article
Field-Weighted Citation Impact
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article

Standby magnetic immunity calculator for STT-MRAM

Marco Mansueto, Steven Soss, Philipp Scharf, A. Talapatra et al.
AIP Advances
Magnetic Field Sensors Techniques
article

Standby magnetic immunity calculator for STT-MRAM

Marco Mansueto, Steven Soss, Philipp Scharf, A. Talapatra, F. Schlaphof, Johannes Müller, A. Zaka, D. Sanchez Hazen
article en

Abstract

This work investigates the impact of external magnetic fields, field orientation, temperature, and exposure time on the standby magnetic immunity (SMI) of spin-transfer torque magnetoresistive random-access memory. Wafer-level measurements were performed using a 3D vector magnet, ensuring uniform magnetic fields on unpackaged and unshielded wafers. The analytical model, used to predict the SMI safety field for long-term exposure, has been validated against hardware data and micromagnetic simulations. Furthermore, we introduce a machine-learning-based SMI calculator that provides a fast, automated estimation of the safety field for complex mission profiles. This approach bridges physics-based modeling and data-driven prediction, providing a practical tool for estimating the SMI of MRAM for automotive and other safety-critical environments.

AIP AdvancesVol. 16(2)
GlobalFoundries (United States) (US), GlobalFoundries (Germany) (DE)
Affordable and clean energy
Openalex Percentile: Top 64%
Magnetic Field Sensors Techniques
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