2021/07/18 by Supriyo Bandyopadhyay, Jayasimha Atulasimha, Anjan Barman
Engineering · Materials Science · Physics and Astronomy · #Advanced Memory and Neural Computing #Computer science #Condensed matter physics #Electrical engineering #Engineering #Ferromagnetism #Magnetic field #Magnetic properties of thin films #Magnetization #Multiferroics and related materials #Nanomagnet #Physics #Spintronics #cond-mat.mes-hall #physics.app-ph
paper · pdf · doi:10.1063/5.0062993
published as Applied Physics Reviews, 8, 041323 (2021) · Invited review article on straintronics
arxiv created 2021/07/18 · openalex publication_date 2021/12/01 · openalex created_date 2021/12/31 · arxiv updated 2022/01/27 · openalex updated_date 2026/08/06
The desire to perform information processing, computation, communication, signal generation, and related tasks, while dissipating as little energy as possible, has inspired many ideas and paradigms. One of the most powerful among them is the notion of using magnetostrictive nanomagnets as the primitive units of the hardware platforms and manipulating their magnetizations (which are the state variables encoding information) with electrically generated static or time-varying mechanical strain to elicit myriad functionalities. This approach has two advantages. First, information can be retained in the devices after powering off since the nanomagnets are nonvolatile unlike charge-based devices, such as transistors. Second, the energy expended to perform a given task is exceptionally low since it takes very little energy to alter magnetization states with strain. This field is now known as “straintronics,” in analogy with electronics, spintronics, valleytronics, etc., although it pertains specifically to “magnetic” straintronics and excludes phenomena involving non-magnetic systems. We review the recent advances and trends in straintronics, including digital information processing (logic), information storage (memory), domain wall devices operated with strain, control of skyrmions with strain, non-Boolean computing and machine learning with straintronics, signal generation (microwave sources) and communication (ultra-miniaturized acoustic and electromagnetic antennas) implemented with strained nanomagnets, hybrid straintronics–magnonics, and interaction between phonons and magnons in straintronic systems. We identify key challenges and opportunities, and lay out pathways to advance this field to the point where it might become a mainstream technology for energy-efficient systems.