Koichi Oyanagi

With the giant magnetoresistance (GMR) effect serving as a vital component in modern spintronic technologies, researchers are dedicating significant efforts to improve the performance of GMR devices through material exploration and design optimization. However, traditional GMR measurement approaches are inefficient for comprehensive material and device optimization. This study proposes a high‐throughput current‐in‐plane GMR measurement technique based on thermal imaging of Joule heating utilizing lock‐in thermography (LIT). This LIT‐based technique is advantageous for efficiently evaluating films with varying compositions and thickness gradients, which is crucial for ongoing material exploration and design optimization to enhance the GMR ratio. First, it is demonstrated that using CoFe/Cu multilayers, the simple Joule heating measurement based on LIT enables quantitative estimation of the GMR ratio. Then, to confirm the usefulness of the proposed method in high‐throughput material screening, a case study is shown to investigate the GMR of CoCu‐based granular films with a composition gradient. These techniques allow to determine the optimum composition with maximum GMR ratio using the single composition‐gradient film and reveal Co₂₂Cu₇₈ as the optimal composition, yielding the largest GMR ratio among the reported polycrystalline CoCu‐based granular films. This demonstration accelerates the material and structural optimization of GMR devices.

We have investigated the anomalous Ettingshausen effect (AEE) in iron-carbon alloys, i.e., cast irons and steel, using the lock-in thermography. All the alloys exhibit the clear AEE-induced temperature modulation, and their anomalous Ettingshausen coefficient is an order of magnitude greater than that of the pure iron at room temperature. The dimensionless figure of merit for AEE in the ductile cast iron is 55 times greater than that in the pure iron owing to the significant increase of the anomalous Ettingshausen coefficient. Our result reveals a potential of iron-carbon alloys as transverse thermoelectric materials, although the composition and microstructures optimizations are necessary.

We have investigated the anomalous Ettingshausen effect (AEE) in iron–carbon alloys, i.e., cast irons and steel, using the lock-in thermography. All the alloys exhibit the clear AEE-induced temperature modulation, and their anomalous Ettingshausen coefficient is an order of magnitude greater than that of the pure iron at room temperature. The dimensionless figure of merit for AEE in the ductile cast iron is 55 times greater than that in the pure iron owing to the significant increase in the anomalous Ettingshausen coefficient. Our result reveals a potential of iron–carbon alloys as transverse thermoelectric materials, although the composition and microstructures optimizations are necessary.

When the electric conductance of a nano-sized metal is measured at low temperatures, it often exhibits complex but reproducible patterns as a function of external magnetic fields called quantum fingerprints in electric conductance. Such complex patterns are due to quantum–mechanical interference of conduction electrons; when thermal disturbance is feeble and coherence of the electrons extends all over the sample, the quantum interference pattern reflects microscopic structures, such as crystalline defects and the shape of the sample, giving rise to complicated interference. Although the interference pattern carries such microscopic information, it looks so random that it has not been analysed. Here we show that machine learning allows us to decipher quantum fingerprints; fingerprint patterns in magneto-conductance are shown to be transcribed into spatial images of electron wave function intensities (WIs) in a sample by using generative machine learning. The output WIs reveal quantum interference states of conduction electrons, as well as sample shapes. The present result augments the human ability to identify quantum states, and it should allow microscopy of quantum nanostructures in materials by making use of quantum fingerprints.