Balram Singh | Searcharxiv

arXiv2026

Geometric dependence of exchange bias in tilted three-dimensional CoFe/IrMn microwires

The exchange bias (EB) effect, arising from interfacial coupling between ferromagnetic (FM) and antiferromagnetic (AF) layers, induces a unidirectional magnetic anisotropy and underpins a wide range of spintronic functionalities. Extending the EB effect to three-dimensional (3D) architectures enables investigation of interfacial coupling in non-planar structures, which is a key step toward realizing spintronic functionalities beyond planar systems. Achieving this requires the fabrication of FM/AF bilayers with smooth interfaces and well-defined thicknesses on non-planar scaffolds, together with suitable characterization methods. In this work, we realize exchange-biased 3D FM/AF microwires by combining two-photon lithography with magnetron sputtering. CoFe/IrMn bilayers are deposited on microwire scaffolds with inclination angles of 0 deg, 30 deg, 45 deg relative to the substrate, and their magnetization reversal is probed using dark-field magneto-optical Kerr effect (DF-MOKE) magnetometry. We find that the EB and coercive fields vary in a characteristic way with the inclination angle, consistent with the systematic reduction in film thickness expected from inclined directional deposition. In addition, the EB magnitude is influenced by the combined effects of surface roughness of non-planar geometries and the directional growth of the bilayer, highlighting the importance of 3D scaffold surface quality for integrating magnetic multilayers. These results provide insight into the growth and magnetic behavior of sputter-deposited magnetic multilayers with functional interfaces on 3D geometries.

cond-mat.mtrl-sci↗

arXiv2025

Interpretable Plant Leaf Disease Detection Using Attention-Enhanced CNN

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, the model enhances feature extraction and disease localization. Trained on five diverse plant disease datasets, our approach outperforms recent techniques, achieving high accuracy (up to 98.87%) and demonstrating robust generalization. Here, we show the effectiveness of our method through comprehensive evaluation and interpretability analysis using CBAM attention maps, Grad-CAM, Grad-CAM++, and Layer-wise Relevance Propagation (LRP). This study advances the application of explainable AI in agricultural diagnostics, offering a transparent and reliable system for smart farming. The code of our proposed work is available at https://github.com/BS0111/PlantAttentionCBAM.

cs.CV↗