Key Role of Micron-Scale Strain Distributions in Magnetoelectric Multiferroic Devices Revealed

Significance 

Composite multiferroic systems, made up by combining ferroelectric/piezoelectric and ferromagnetic materials, are of ultimate importance in controlling the orientation of on-chip magnetic nanostructures with electric fields from patterned electrodes. This makes such materials systems of broad technological interest, since they offer a path toward the development of ultralow power magnetoelectric devices. The key aspect of these systems is the possibility to control magnetization in a very energy efficient way through an electric field, relying on the magneto-elastic coupling at the interface between the piezoelectric and the ferromagnetic components. Unfortunately, however, identical magnetic structures on a single piezoelectric substrate have been reported to not exhibit a uniform behavior in response to the electrical stimulus, as theoretically predicted for an ideal case. This nonuniform behavior has been usually attributed to the presence of non-uniform micron-scale strain across the chip surface. Up to date, no quantitative experimental measurement has yet interrogated the systematic effect of this non-uniform micron-scale strain on magnetic microstructures as a function of the applied electric field.

Researchers led by Professor Jeffrey Bokor from the Department of Electrical Engineering and Computer Science at the University of California Berkeley, working under the auspices of the National Science Foundation-supported Center for Translational Applications of Nanoscale Multiferroic Systems (TANMS), conducted a study where they thoroughly characterized the micron-scale strain and magnetic response, as a function of an applied electric field, in a composite multiferroic system. Their goal was to come up with a comprehensive behavior and understanding of these materials using direct imaging of both the electrically induced magnetic behavior and the piezo-strain. Their work is now published in the research journal, Nano Letters.

The research study commenced with the imaging of the onset of an electrically induced magnetic reorientation in an array of Ni micro-squares at room temperature using X-ray magnetic circular dichroism−photoemission electron microscopy. The research team observed a reorientation of the magnetic moments in the nickel squares along the main compressive strain direction of the piezoelectric PMN−PT crystal substrate. Consequently, the piezo-strain along the three main crystallographic directions of the substrate was measured by X-ray microdiffraction, with a micrometer lateral resolution, as a function of the applied electric field. This allowed the researchers to obtain a direct image of the piezo-strain distribution at the same location of the previously imaged magnetic squares. What was found is that the strain acting onto the identical magnetic squares was non-uniform, with a variation happening at the micrometer scale.

From the two complementary measurements on the same area on the sample, the authors observed that the presence of a non-uniform strain strongly influenced the reorientation of the magnetic state within identical nickel microstructures along the surface of the sample. Moreover, the connection between the onset of magnetic reorientation and the locally induced piezo-strain was unveiled from the image analysis which were further validated by micro-magnetic simulations.

It was very satisfactory to see how the magnetic structures activation map and the piezo-strain distribution map overlapped to each other” said Dr. Roberto Lo Conte, the lead author who participated to the study as a postdoctoral researcher at the University of California, Berkeley. “We finally had a direct proof of what had been proposed in the scientific community for years. The whole team was very enthusiastic about the result”.

Roberto Lo Conte and colleagues’ research work demonstrated a systematic micron-scale study of the physical mechanisms which drive a PMN−PT/Ni multiferroic actuator. It was seen that when an electric field was applied uniformly over the surface, areas separated by microns were observed with distinctly different strain amplitudes driving significant local spatial variations of the magnetoelastic anisotropy and resulting in the non-uniform activation of identical magnetic Ni micro-squares. Altogether, their systematic study has emphasized on the crucial importance of surface and interface engineering on the micron-scale in composite multiferroic structures and has in turn introduced a robust technique to characterize future devices on these length scales.

 

Key Role of Micron-Scale Strain Distributions in Magnetoelectric Multiferroic Devices Revealed. Advances in Engineering

 

About the author

Jeffrey Bokor is the Paul R. Gray Distinguished Professor of Engineering in the department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, with a joint appointment as Senior Scientist in the Materials Science Division at Lawrence Berkeley National Laboratory.

He also serves as Chair of the Electrical Engineering Division in the EECS Department. He received the B.S. degree in electrical engineering from the Massachusetts Institute of Technology in 1975, and the M.S. and Ph.D. degrees in electrical engineering from Stanford University in 1976 and 1980, respectively. From 1980 to 1993, he was at AT&T Bell Laboratories where he did research on a variety of topics in laser science, surface science, advanced lithography for integrated circuits, as well as semiconductor physics and technology, and held several management positions. He joined the Berkeley faculty in 1993. From 2012 to 2017, he served as Associate Dean for Research in the College of Engineering.

His current research activities include nanomagnetics/spintronics, graphene electronics, nanophotonics, and nano-electromechanical systems. He is a fellow of IEEE, APS, and OSA.

About the author

Dr Lo Conte’s scientific interests focus on studying new magnetic materials systems useful for the development of energy efficient spintronic devices. He began is academic career in Italy, obtaining his bachelor degree and subsequently his master degree in Physics Engineering at the Politecnico di Milano, with a final project focused on the fabrication and characterization of a magneto-optic device for the development of a metallic spin-flip based laser. Such a project was carried out at the Royal Institute of Technology (KTH) in Stockholm, Sweden, where Dr. Lo Conte spent two years as a Double Degree student and obtained his Master of Science in Engineering degree. In 2012 he moved to Germany for his PhD in Applied Physics at the Johannes Gutenberg University of Mainz, where he graduated in 2015 with a thesis on “Magnetic nanostructures with structural inversion asymmetry”.

In 2016 he joined the University of California at Berkeley as a post-doctoral researcher in the Electrical Engineering and Computer Science department, where he investigated multiferroic heterostructures with the intent of developing new magnetoelectric technologies for energy efficient applications.

Today Dr Lo Conte is a Marie Curie Fellow at the University of Hamburg in Germany and a post-doctoral research associate at the University of California at Berkeley, in the Materials Science and Engineering department, studying magnetic multilayers hosting topologically non-trivial spin states.

Reference

Roberto Lo Conte, Zhuyun Xiao, Cai Chen, Camelia V. Stan, Jon Gorchon, Amal El-Ghazaly, Mark E. Nowakowski, Hyunmin Sohn, Akshay Pattabi, Andreas Scholl, Nobumichi Tamura, Abdon Sepulveda, Gregory P. Carman, Robert N. Candler, and Jeffrey Bokor. Influence of Nonuniform Micron-Scale Strain Distributions on the Electrical Reorientation of Magnetic Microstructures in a Composite Multiferroic Heterostructure.Nano Lett. 2018, 18, 1952−1961

Go To Nano Lett

Check Also

Light-Speed Encryption: Unlocking the Future with Spatially Incoherent Diffractive Neural Networks - Advances in Engineering

Light-Speed Encryption: Unlocking the Future with Spatially Incoherent Diffractive Neural Networks