Spintronics: From Devices to Circuits to Systems

Spintronics technology provides an exciting platform for implementing computational structures, and recent work has demonstrated the potential for leveraging its nonvolatility properties to build energy-efficiency systems. This talk presents a view of the state of the art in this field, as well as a view of cutting-edge research directions. We will present results from our collaborative efforts involving physicists, material scientists, circuit designers, and architects, which have led to the development of novel device structures, circuits, and memory arrays. Together, these help construct viable pathways for building spin-based structures for computation, memory, and in-memory computation, including for AI applications.

Spin-based memories are nonvolatile and are conventionally based on arrays of magnetic tunneling junctions (MTJs). The talk will first show the current state of technology for building spin-based memories, and then present directions for next-generation improvements in spintronic memory technologies. We will then present spin-based structures that have also been shown to be highly efficient for logic applications in specific scenarios, such as those that require nonvolatility or are used for error resilient applications. Finally, we will show methods for building spin-based compute-in-memory structures that are greatly advantageous for data-intensive applications, and demonstrate the efficiencies that can be achieved by this model for a neuromorphic application.