Evidence for shallow implantation during the growth of bismuth nanocrystals by pulsed laser deposition

The shallow implantation of Bi species was analyzed for energy densities above 2 Jcm-2. The implantation range was shown to depend on the energy density used for ablation, which was related to the velocity of the Bi atoms and ions in the plasma. The kinetic energy of the Bi species in the plume generated at laser energy densities above 2 J cm-2 was estimated to be around 200 eV.This work has been partially supported by project TIC99-0866, CICYT (Spain). One of the authors (J.-P.B.) acknowledges support by the EPSRC and a Marie Curie Fellowship of the EC under Contract No. HPMT-CT-2000-00064.Peer Reviewe

Spin injection from the Heusler alloy Co_2MnGe into Al_0.1Ga_0.9As/GaAs heterostructures

Electrical spin injection from the Heusler alloy Co_2MnGe into a p-i-n Al_0.1Ga_0.9As/GaAs light emitting diode is demonstrated. A maximum steady-state spin polarization of approximately 13% at 2 K is measured in two types of heterostructures. The injected spin polarization at 2 K is calculated to be 27% based on a calibration of the spin detector using Hanle effect measurements. Although the dependence on electrical bias conditions is qualitatively similar to Fe-based spin injection devices of the same design, the spin polarization injected from Co_2MnGe decays more rapidly with increasing temperature.Comment: 8 pages, 4 figure

Quantum materials for energy-efficient neuromorphic computing

Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems