Spintronic Quantum Materialization and Learning-lab (SQML) aims to realize energy and time-efficient hardware using spintronic and quantum materials for physical and quantum intelligence. Currently, we are focusing on novel electronic and spintronic materials and their hardware-software co-design for memory, AI, robotic, and quantum computing applications.

Here are a few keywords describing our research topics:

  • spin-orbit torque
  • topological insulator
  • spin-charge interconversion
  • skyrmion in ferromagnetic, ferrimagnetic and antiferromagnetic multilayers
  • spin Hall effect
  • magnetic proximity effect
  • in-memory computing
  • quantum computing
  • neuromorphic computing
  • compact model
  • circuit design
  • device-system co-optimization (DSCO)
  • robotic learning

The techniques that we use frequently are the following: