No abstract

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 Perspective discusses select examples of these approaches and provides an outlook on the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.

We investigate the possibility of modulating the magnetic properties of (Ga,Mn)As digital ferromagnetic heterostructures (DFHs) via strain engineering. We p-dope DFHs below the compensation threshold of residual As antisites to achieve variations in strain without introducing free carriers and with relatively modest concentrations of impurity atoms. X-ray diffraction and superconducting quantum interference device measurements reveal a trend toward higher TC as the out-of-plane strain is increased. Additionally, we demonstrate a second method for strain engineering wherein DFHs are grown on anisotropically relaxed (Ga,In)As stressor layers. We show that the ferromagnetic properties are independent of strain in this regime and conclude that the structure-dependent modulation of magnetic properties in DFHs cannot be explained by simple strain effects alone.