Robustness of Voltage-induced Magnetocapacitance

Electromagnetic Radiation Effects on MgO-Based Magnetic Tunnel Junctions: A Review

Magnetic tunnel junctions (MTJs) have been widely utilized in sensitive sensors, magnetic memory, and logic gates due to their tunneling magnetoresistance. Moreover, these MTJ devices have promising potential for renewable energy generation and storage. Compared with Si-based devices, MTJs are more tolerant to electromagnetic radiation. In this review, we summarize the functionalities of MgO-based MTJ devices under different electromagnetic irradiation environments, with a focus on gamma-ray radiation. We explore the effects of these radiation exposures on the MgO tunnel barriers, magnetic layers, and interfaces to understand the origin of their tolerance. This review enhances our knowledge of the radiation tolerance of MgO-based MTJs, improves the design of these MgO-based MTJ devices with better tolerances, and provides information to minimize the risks of irradiation under various irradiation environments. This review starts with an introduction to MTJs and irradiation backgrounds, followed by the fundamental properties of MTJ materials, such as the MgO barrier and magnetic layers. Then, we review and discuss the MTJ materials and devices' radiation tolerances under different irradiation environments, including high-energy cosmic radiation, gamma-ray radiation, and lower-energy electromagnetic radiation (X-ray, UV-vis, infrared, microwave, and radiofrequency electromagnetic radiation). In conclusion, we summarize the radiation effects based on the published literature, which might benefit material design and protection.

Large magnetocapacitance beyond 420% in epitaxial magnetic tunnel junctions with an MgAl2O4 barrier

Magnetocapacitance (MC) effect has been observed in systems where both symmetries of time-reversal and space-inversion are broken, for examples, in multiferroic materials and spintronic devices. The effect has received increasing attention due to its interesting physics and the prospect of applications. Recently, a large tunnel magnetocapacitance (TMC) of 332% at room temperature was reported using MgO-based (001)-textured magnetic tunnel junctions (MTJs). Here, we report further enhancement in TMC beyond 420% at room temperature using epitaxial MTJs with an MgAl₂O₄(001) barrier with a cation-disordered spinel structure. This large TMC is partially caused by the high effective tunneling spin polarization, resulted from the excellent lattice matching between the Fe electrodes and the MgAl₂O₄ barrier. The epitaxial nature of this MTJ system sports an enhanced spin-dependent coherent tunneling effect. Among other factors leading to the large TMC are the appearance of the spin capacitance, the large barrier height, and the suppression of spin flipping through the MgAl₂O₄ barrier. We explain the observed TMC by the Debye-Fröhlich modelled calculation incorporating Zhang-sigmoid formula, parabolic barrier approximation, and spin-dependent drift diffusion model. Furthermore, we predict a 1000% TMC in MTJs with a spin polarization of 0.8. These experimental and theoretical findings provide a deeper understanding on the intrinsic mechanism of the TMC effect. New applications based on large TMC may become possible in spintronics, such as multi-value memories, spin logic devices, magnetic sensors, and neuromorphic computing.

Observation and theoretical calculations of voltage-induced large magnetocapacitance beyond 330% in MgO-based magnetic tunnel junctions

Magnetic tunnel junctions (MTJs) in the field of spintronics have received enormous attention owing to their fascinating spin phenomena for fundamental physics and potential applications. MTJs exhibit a large tunnel magnetoresistance (TMR) at room temperature. However, TMR depends strongly on the bias voltage, which reduces the magnitude of TMR. On the other hand, tunnel magnetocapacitance (TMC), which has also been observed in MTJs, can be increased when subjecting to a biasing voltage, thus exhibiting one of the most interesting spin phenomena. Here we report a large voltage-induced TMC beyond 330% in MgO-based MTJs, which is the largest value ever reported for MTJs. The voltage dependence and frequency characteristics of TMC can be explained by the newly proposed Debye-Fröhlich model using Zhang-sigmoid theory, parabolic barrier approximation, and spin-dependent drift diffusion model. Moreover, we predict that the voltage-induced TMC ratio could reach over 3000% in MTJs. It is a reality now that MTJs can be used as capacitors that are small in size, broadly ranged in frequencies and controllable by a voltage. Our theoretical and experimental findings provide a deeper understanding on the exact mechanism of voltage-induced AC spin transports in spintronic devices. Our research may open new avenues to the development of spintronics applications, such as highly sensitive magnetic sensors, high performance non-volatile memories, multi-functional spin logic devices, voltage controlled electronic components, and energy storage devices.

Spintronic Sensors Based on Magnetic Tunnel Junctions for Wireless Eye Movement Gesture Control

The tracking of eye gesture movements using wearable technologies can undoubtedly improve quality of life for people with mobility and physical impairments by using spintronic sensors based on the tunnel magnetoresistance (TMR) effect in a human-machine interface. Our design involves integrating three TMR sensors on an eyeglass frame for detecting relative movement between the sensor and tiny magnets embedded in an in-house fabricated contact lens. Using TMR sensors with the sensitivity of 11 mV/V/Oe and ten <1 mm³ embedded magnets within a lens, an eye gesture system was implemented with a sampling frequency of up to 28 Hz. Three discrete eye movements were successfully classified when a participant looked up, right or left using a threshold-based classifier. Moreover, our proof-of-concept real-time interaction system was tested on 13 participants, who played a simplified Tetris game using their eye movements. Our results show that all participants were successful in completing the game with an average accuracy of 90.8%.