Quantum Matter

Our group investigates novel physical phenomena, topological and mathematical concepts in quantum and classical systems. We use various methods from materials growth, device fabrication, to comprehensive characterizations. We explore new materials that have unconventional properties with potential application at room temperature, such as topological phases of quantum matter, strong spin-orbit coupled electronic materials, and magnetic systems. We also focus on the detection and manipulation of the relevant quantum effects for high speed/density non-volatile memory and logic devices as well as quantum computation.

Our current research topics include:
  • (Magnetic) Topological insulator
  • Quantum anomalous Hall insulator
  • Antiferromagnetic spintronics
  • Topological superconductors and Majorana fermions
  • Skyrmions, vortices, and other spin textures


    Spintronics is a field of research that explores the use of electron spin as a degree of freedom for information processing. Spintronics devices can offer advantages such as non-volatility, low power consumption and high speed. One of the main challenges in spintronics is to create and manipulate spin-polarized currents in semiconductors, which are the building blocks of modern electronics.

    Our group is interested in developing spintronics devices based on germanium (Ge), a semiconductor with high carrier mobility and compatibility with silicon technology. We use a tunnel junction composed of iron (Fe), magnesium oxide (MgO) and Ge to inject spin-polarized electrons into Ge. The MgO layer acts as a spin filter that enhances the spin polarization of the tunneling electrons. The Fe/MgO/Ge junction has a high quality interface that allows efficient electronic transport and avoids Fermi level pinning. We also use surface doping techniques to increase the conductivity of Ge and facilitate tunneling transport. We are currently investigating the spin injection and transport properties of Ge using various experimental methods.

    2D Materials

    2D materials are thin layers of atoms that have unique physical properties due to their reduced dimensionality. They have been widely studied since the discovery of graphene, a single layer of carbon atoms, in 2004. Our 2-D group is interested in exploring the novel phenomena and applications of 2D materials in various fields of physics.

    Our research focuses include
    • Surface condition and its effect on electron and spin transport.
    • Vertical transport behavior in non-periodic atomic structures.
    • 2 D topologic transition and its application in quantum state manipulation.
    • Electron-phonon interaction in low dimensional systems.
    Our instruments include
    • Home-built cryogenic Raman system
    • AMI electron transport characterization system
    • Cryogenic optoelectronic characterization system

    Collective Behavior & Neromorphics

    Neurodynamical behavior

    We use spike recordings of the brain to study how it behaves under different conditions, such as neuro-related diseases. We also use neuromorphic chips to simulate and predict brain activity with different parameters and randomness. This helps us to create a phase diagram of brain dynamics that shows the different states and transitions of the neural system.

    Combining Shannon information with neuromorphics

    We combine functions and computational activity of today’s computers with behavior observed from neurons and study its effect on both conventional computing tasks and machine learning tasks. For example, we modify an algorithm such that its activation is similar to neuron activation in the brain to achieve associative computing. This approach can potentially enhance the performance and efficiency of computing systems and enable new applications in artificial intelligence.

    Novel computing fabrics

    One of the challenges in computing is finding efficient ways to solve complex optimization problems. We explore new, coupled computing devices such as magnetic dots and chaotic oscillators for generating dynamic patterns and manipulating their properties by controlling network parameters. For example, in an annealing-based algorithm, system-driven chaos has the ability to escape local minima; while a system driven towards equilibrium can be used to conduct a fine search around the solution space. These devices offer novel possibilities for parallel and adaptive computing that can exploit the rich dynamics of nonlinear systems.