Spintronic materials and devices for computing, memory, and machine intelligence:
We work on innovative materials and nano-devices which go far beyond existing ones in energy efficiency, speed, and integration density. In particular, here we use materials and devices that couple electrons' spin and charge properties, also called spintronic devices. Our goal is to beat today's performance, energy, and scaling limits of data storage, memory, and computing devices, each by 100x. For example, can we build memories that simultaneously operate at pico-Second read/write, atto-Joule per bit operation, and at few-nanometer scales? This work involves new materials development, understanding of physics of thin films and interfaces, and their consequent electrical and magnetic behavior.
Observation of current-induced switching in non-collinear antiferromagnetic IrMn3 by differential voltage measurements, Nature Communications, Vol. 12, p. 3828, 2021.
Electrical manipulation of the magnetic order in antiferromagnetic PtMn pillars, Nature Electronics, Vol. 3, pp. 92-98, 2020.
Picosecond electric-field-induced switching of antiferromagnets, Physical Review Applied, Vol. 11, p. 024019, 2019.
Switching of perpendicular magnetization by spin–orbit torques in the absence of external magnetic fields, Nature Nanotechnology, Vol. 9, p. 548, 2014.
Microwave, magnonic, quantum, and sensing devices:
We are also interested in using spintronic effects to build microwave, radio-frequency, and sensing devices. Examples are record-small electronic oscillators using spin-torque, record-sensitive detectors of microwave radiation, and magnonic devices based on spin waves, which are the collective excitations of spins in a magnetically ordered material.
Giant spin-torque diode sensitivity in the absence of bias magnetic field, Nature Communications, Vol. 7, p. 11259, 2016.
Unconventional computing architectures:
Here we focus on using the new devices which we develop, in novel architectures for computing systems, and to realize circuits/architectures where new intelligent functionalities emerge from the physics. We work closely with industry and interact across disciplines, to develop the hardware, tools and ecosystems to translate device/circuit advances into applications.
Implementation of Artificial Neural Networks using Magnetoresistive Random-Access Memory-based Stochastic Computing Units", IEEE Magnetics Letters, 2021.
Array-Level Analysis of Magnetoelectric Random Access Memory for High-Performance Embedded Applications, IEEE Magnetics Letters, 2017.