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
- MRAM (STT/SOT/VCMA)
- 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:
- second harmonic method
- spin-torque ferromagnetic resonance
- Differential Kerr method
- Magneto-optical Kerr effect (MOKE) microscopy
- X-ray magnetic circular dichroism
- Polarized neutron reflectometry
- magnetron sputtering
- pulsed laser deposition
- micromagnetic simulation
- physical device modeling
- circuit design
- system design and prototyping
- device-system co-optimization (DSCO)
- machine learning
- ROS