Harnessing Quantum Materials to design Antiferromagnetic Topological Textures

The computing ecosystem uses 10% of the global electricity and contributes to 2% emissions (at par with aviation). Left unchecked, this demand is expected to rise rapidly to 21% by 2030. Hence, for energy sustainability, it is critical to develop computing platforms with dense, fast yet energy-efficient information storage and processing. An emerging candidate that can address these needs is spintronic memory and logic, which harnesses whirling magnetic topological textures (TTs) as dynamic information bits. In the last decade significant progress was made in developing ferromagnetic (FM) TTs. However, their practical utility has been inhibited by susceptibility to stray magnetic fields, strong internal dipolar fields, slow speeds and sideway motion. To alleviate these issues, there has been a surge of interest in discovering antiferromagnetic (AFM) analogues, which are predicted to be robust, scalable, ultra-fast and energy-efficient.

We have recently made the pioneering demonstration of a family of AFM TTs at room temperature. To harness them practically, it is now crucial to develop targeted electrical control pathways. To this effect, HQ-AFM will build a novel quantum materials platform that affords exquisite all-electrical control of homochiral AFM TTs via emergent interfacial phenomena. First, I will design multiferroic heterostructures, containing an epitaxial AFM layer sandwiched between ferroelectric (FE) and heavy-metal (HM) layers, hosting symmetry-breaking interactions to stabilize homochiral TTs. Then, I will exploit FE switching to realize electric-field tuning of their chirality, size and stability. Lastly, I will harness current-based spin-orbit torques injected from the HM layer to trigger their nucleation and ultra-fast motion. HQ-AFM will thus enable non-volatile, reversible and scalable control of AFM TTs, pushing the knowledge frontiers of AFM topological spintronics and forging the path to energy-efficient "beyond-Moore" computing paradigm.