Domain‐Engineered Flexible Ferrite Membrane for Novel Machine Learning Based Multimodal Flexible Sensing

Abstract

To run into different usage scenarios, tremendous kinds of bending sensors based on resistive, capacitive, piezoelectric responses, or optical theories have been prepared, which encompass a broad scope of novel materials, such as carbon nanotubes, metallic nanowires, carbonized silk fabrics, and fiber-optics, etc. [7][8][9][10] Despite those impressive performances, the development of novel flexible materials that possess multimodal sensing capabilities for the simultaneous detection of bending curvature and position information remains challenging, which is very critical in novel adhesion-free flexible sensing systems. [11,12] Flexible magnetic films have great potential for wireless multimodal flexible sensor due to the curvature and azimuth angle dependent magnetic anisotropy. [13][14][15][16] Former studies have introduced the sensing functions like correcting localization of the plastic deformation on the sample surface. [17,18] To realize the bending sensing function, ferromagnetic resonance has been reported as an effective sensing technology due to the strong correlation between mechanical bending and magnetic anisotropy. [19] In addition, the microwave absorption performance endows it with great potential in wireless sensing. [20] For instance, Figure 1a shows the 1D sensing relationship between bending curvature radius R and ferromagnetic resonance field (H r ) for flexible high-quality epitaxial Flexible materials and devices that can simultaneously reflect multimodal information are highly desired for novel flexible electronics and intelligent flexible sensing systems. In this regard, flexible magnetic films have great potential for wireless multimodal flexible sensor due to the curvature and azimuth angle-dependent ferromagnetic resonance. However, a key challenge now is to build the precise relationship among the mechanical bending, azimuth angle, and the ferromagnetic resonance of the film, which involves multi-physics and coupled process. In this work, the physical problem is solved by combining material engineering and machine learning. Material domain engineering is applied to form localized multi-peak ferromagnetic resonance features for increasing sensitivity. Besides, convolutional neural network algorithm is utilized to help recognize the bending and azimuth angle modulated ferromagnetic resonance in flexible film systems. It is found that the bending information for the flexible film with engineered domain structure can be mapped to the ferromagnetic profile with accuracy over 99%, while the accuracy sharply decreases to less than 50% in the control group of high-quality film. This study provides a versatile platform for developing machine learning-based novel sensing materials.

Latest developments and trends in electronic skin devices

“…ML methods can greatly enhance the intelligence level of an e-skin system, which can greatly improve the performance of human-machine interfaces (HMI), and show a broad application prospect in medical health, rehabilitation therapy and remote monitoring [201][202][203][204] . They can learn feature signals corresponding to a certain stimulus from a large amount of experimental data, which can recognize different types of stimuli (such as gesture, touch strength, texture, and shape) [205][206][207][208] .…”

Bending Modulated Ultralarge Magnetoresistance in Flexible La0.67Ba0.33MnO3 Thin Film Based Device

“…Magnetoresistance (MR), which presents the change of resistance under external magnetic field, can reflect rich physical origins for spintronic information devices. , In MR devices including nonvolatility memories, sensors for current or angle, filters, etc., the MR ratios relate to the sensitivity, stability, and precision features. , Especially for emerging information technologies such as Internet of Things, flexible sensing, − and artificial intelligence, , high MR ratios can significantly improve the performance of these novel applications, which is the core goal pursued by physicists and materials scientists. To obtain high MR ratios, different types of MR devices including anisotropic MR, giant MR, colossal MR, tunneling MR, and extremely large MR (XMR) have been continuously developed and improved .…”