RESUMEN
Terahertz emission from ferromagnetic/non-magnetic spintronic heterostructures had been demonstrated as pump wavelength-independent. We report, however, the pump wavelength dependence of terahertz emission from an optimized Fe/Pt spintronic bilayer on MgO substrate. Maximum terahertz generation per total pump power was observed in the 1200- to 1800-nm pump wavelength range, and a marked decrease in the terahertz emission efficiency beyond 2500 nm (pump photon energies <0.5 eV) suggests a â¼0.35-eV threshold pump photon energy for effective spintronic terahertz emission. The inferred threshold is supported by previous theoretical results on the onset energy of significant spin-filtering at the Fe-Pt interface, and confirmed by Fe/Pt electronic structure calculations in this present work. The results of terahertz time-domain emission spectroscopy show the sensitivity of spintronic terahertz emission to both the optical absorptance of the heterostructure and the energy-dependent spin transport, as dictated by the properties of the metallic thin films.
RESUMEN
Several machine learning (ML) techniques were tested for the feasibility of performing automated pattern and waveform recognitions of terahertz time-domain spectroscopy datasets. Out of all the ML techniques under test, it was observed that random forest statistical algorithm works well with the THz datasets in both the frequency and time domains. With such ML algorithm, a classifier can be created with less than 1% out-of-bag error for segmentation of rusted and non-rusted sample regions of the image datasets in frequency domain. The degree of linear correlation between the rusted area percentage and the image spatial resolution with terahertz frequency can be used as an additional cross-validation criteria for the evaluation of classifier quality. However, for different rust staging measured datasets, a standardized procedure of image pre-processing is necessary to create/apply a single classifier and its usage is only limited to 1 ± 0.2 THz. Moreover, random forest is practically the best choice among the several popular ML techniques under test for waveform recognition of time-domain data in terms of classification accuracy and timing. Our results demonstrate the usefulness of random forest and several other machine learning algorithms for terahertz hyperspectral pattern recognition.
RESUMEN
We are reporting a new type of compact magneto-optic sensor constructed from terahertz-wave spintronic emitter and electro-optic detector. The corresponding terahertz polarization output of the emitter and the detection phase-sensitivity of the detector depend on the vector of the external magnetic field. The emitter/detector pair consists of two small and thin wafers sandwiched together and capped with a thin gold mirror. As a result, the use of bulky terahertz steering/collection optics was completely eliminated in our magneto-optic imager. With such simple on-chip generation/detection scheme for terahertz time-domain setup in reflection-type geometry, we were able to record the raster-scanned image contrast of a permanent magnet in the proximity of the sensor surface. The contrast strongly varies with the magnet orientation and its position with respect to the sensor. The imager spatial resolution depends on chip optical quality for tight femtosecond-laser pump/probe cross-focusing at detector/mirror interface and terahertz generation/detection efficiency. In this respect, the chip robustness to the pump/probe fluences is also an important factor to consider.