Optimizing Heusler Alloys for large Magnetoresistance and Spin-Transfer Torque in Devices

What is it about?

This research focuses on optimizing the composition of Co₂MnₓFe₁₋ₓGe (CMFG) Heusler alloys to enhance the performance of spintronic devices, particularly current-perpendicular-to-plane giant magnetoresistance (CPP-GMR) sensors. By using an innovative high-throughput approach, the study efficiently explores the effects of varying the Mn/Fe ratio on magnetoresistance (MR) and spin-transfer torque (STT) efficiency. A composition-gradient sample was used to investigate the relationship between alloy composition and device performance. Key findings include achieving a record MR ratio of ~35% at 250°C and ~45% at 350°C, along with an STT efficiency of approximately 0.6.

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Photo by Scott Webb on Unsplash

Photo by Scott Webb on Unsplash

Why is it important?

For future high-density hard disk drives (HDDs) and energy-efficient memory technologies, ferromagnetic materials with high spin polarization are crucial. These properties enhance spin-transfer torque (STT) and magnetoresistance (MR), which are the foundational principles of spintronic devices. The compositional optimization of Heusler alloys is key to achieving these properties. This study demonstrates the effectiveness of combinatorial sputtering, composition-gradient samples, microfabrication, and high-throughput automated measurements in optimizing MR and STT efficiency for CPP-GMR devices. Additionally, achieving high structural ordering in Heusler alloys at low annealing temperatures is essential for practical applications, such as CPP-GMR read heads in HDDs, where the processing temperature must remain below 350°C. The CMFG Heusler compounds are an ideal material choice for obtaining highly ordered alloys at low temperatures. By fine-tuning the composition, this research achieves high MR and STT efficiency, providing a framework for developing high-performance spintronic devices for next-generation memory and storage technologies. The approach also generates large datasets for material properties, which can be used for machine learning-based material discovery. This method not only advances spintronic device performance but also offers a scalable framework for optimizing other material systems, enabling more efficient and targeted research for future technologies

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