Replacement of Critical Raw Materials Used in Magnetic Memories and Storages

Thanks to the recent development in the information technology, we have been increasing the data we can use exponentially with exceeding 40 ZB worldwide. The Big Data Era requires further improvement in data processing, storage and communications, namely in terms of their capacity and speed. However these technologies rely on some critical raw materials, such as cobalt, platinum-group metals, rare-earth metals, lithium etc. In this study, we have focused on the replacement of the critical raw materials used in magnetic memories.
Over the past decades, the magnetic memories and storages have been dependant on a magnetic tunnel junction, consisting of Co-based ferromagnetic layers and Ir-based antiferromagnet to pin the magnetisation of one of the ferromagnets. Although the consumption of these critical raw materials per device is small, the total amount the industry uses cannot be ignored. We have been working on the replacement of these layers with more abandoned metals, including Fe and Mn.
In this presentation, we will discuss our recent achievements using machine learning [1] to search candidates to replace both ferromagnetic and antiferromagnetic Heusler alloys. The films were sputtered using ultrahigh vacuum magnetron sputtering on MgO(001) and Si substrates. The structural and magnetic characterisation was done by X-ray diffraction and transmission electron microscopy, and vibrating sample magnetometry, respectively. The optimised films were implemented in a junction for transport measurements. The material search is found to be useful by combining with ab initio calculations on alloys suggested by machine learning.
This work was partially supported by JST-CREST (JPMJCR17J5), JSPS Grants-in-Aid for Scientific Research (23K34567) and ERC Advanced Grant (101097475).
[1] R. Okabe et al., IEEE Magn. Lett. 14, 2500305 (2003).