CAREER: Energy-efficient Magnetic Random Access Memory (MRAM) with All-Superconducting Operations

This project aims to develop a new energy-efficient memory device to address the rapidly growing energy demands of artificial intelligence (AI) and data centers. As AI models become larger and more powerful, the electricity required to train and operate these computational models is growing exponentially and is projected to consume up to 12% of total electricity in the U.S. by 2028. Within this current data-centric computing scheme, non-volatile memory devices that store and retrieve information are a major contributor to energy dissipation. To address this challenge, the project will demonstrate a new cryogenic memory technology - superconducting magnetic random-access memory (SC-MRAM), which can dramatically reduce energy consumption by several orders of magnitude, even when accounting for the refrigeration cost. The proposed SC-MRAM device leverages the zero-resistance, dissipation-less nature of superconducting currents to perform memory read and write operations. The research integrates materials development, investigation of underlying physical mechanisms, and device engineering to establish a scalable pathway toward high-performance cryogenic memory. Such devices could lead to a paradigm shift toward energy-efficient cryogenic data centers with superior computational performance. On the education front, the project also entails a Personalized Education with AI for Quantum Engineering (PEAQ) program to modernize cross-disciplinary workforce training at the intersection of electrical engineering and quantum technologies. The PEAQ effort includes AI-assisted personalized curriculum design, developing intelligent textbooks with interactive learning experiences, and community outreach to increase quantum literacy among the public. Technically, the project seeks to understand and harness how spin-polarized supercurrents can control magnetization in superconductor/ferromagnet heterostructures. The research is organized into three coordinated thrusts. First, it will investigate spin transport and magnetization dynamics in various superconductor/ferromagnet device geometries through carefully designed magneto-transport measurements. Second, it will optimize material stacks and interfaces, with particular emphasis on strongly spin–orbit-coupled superconducting nitrides such as tantalum nitride (TaN), to simultaneously maximize both the spin Hall angle and the superconducting critical current density required for supercurrent-driven switching. Third, peripheral superconducting devices and circuitry, including superconducting diodes and superconducting transistors, will be developed to achieve all-superconducting read and write operations. By combining material optimization, transport characterization, device modeling, and circuit-level validation, the project aims to establish the scientific foundation and technological feasibility of all-superconducting memory, advancing both fundamental knowledge in superconducting spintronics and practical pathways toward cryogenic, energy-efficient computing architectures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2540679 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Gang Qiu | Institution: University of Minnesota-Twin Cities, MINNEAPOLIS, MN | Award Amount: $550,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2540679 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2540679.html