Voltage control and 3D nanomagnetism - towards next generation
spintronic computing

Fueled by the rapid growth of artificial intelligence and big-data-driven computing, information and communications technologies are projected to account for over 20% of the global energy consumption by 2030. This represents a grand challenge to overcome the limitations of current CMOS based digital information processing. To address this, my research focuses on leading directions in spintronic computing that utilizes the non-volatility, energy-efficiency, and nonlinear magnetization dynamics in magnetic nanostructures towards achieving novel computing paradigms.

In this talk, I will first discuss electric field control for magnetism, which is significantly more energy efficient compared to conventional electrical current-based devices. Voltage control of charge density as well as ionic motion is demonstrated to manipulate topological spin textures such as magnetic skyrmions, which have important potential applications in non- volatile memory [1]. 
Next, I will discuss three-dimensional (3D) nanomagnetism, where information can be stored at substantially higher density, and intricate magnetic configurations along with complex magnetization dynamics can be stabilized. Sequential and discrete magnetization switching are demonstrated in 3D quasi-ordered interconnected nanowire (NW) networks, attractive to implement integrated multistate memristors [2]. Furthermore, diverse programming of synaptic weights can be achieved in self-assembled magnetic NW networks, which share close resemblance to actual biological neuron organization, via current driven magnetic domain wall pinning/depinning. Recently, I have been investigating curved thin films to elucidate the fundamental relationship between curvature and chirality in 3D nanomagnetic systems. These studies illustrate the promise of voltage control and 3D nanomagnetism towards energy-efficient nanomagnetic devices for next generation memory and neuromorphic computing applications.

Work supported in part by the NSF (CCF-1909030, ECCS-2151809, DMR-2005108), SRC/NIST nCORE SMART 
Center, and KAUST.
[1] Bhattacharya et al, Nature Electronics, 3 (9), 539 (2020).
[2] Bhattacharya et al, Nano Letters, 22 (24), 10010 (2022).