Magnetic-Based Integrated Sensing and In/Near-Sensor Processing:A Comprehensive Survey and Future Outlook

In recent years, spintronic devices and non-Von Neuman architectures have emerged as two promising approaches to overcome the power, performance, and efficiency limitations of conventional computing architectures. Spintronic devices, such as Magneto-Electric FETs (MEFETs) and Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM), offer unique advantages, including non-volatility, high-speed operation, and low-power characteristics, making them well-suited for various data and compute -intensive applications. On the other hand, in-sensor processing, which integrates computation and sensing into a single platform, offers significant improvements in energy efficiency, latency, and overall system performance by reducing data movement and offloading computation tasks to the sensor level. In this survey paper, we provide a comprehensive overview of recent advancements in magnetic-based integrated sensing and processing schemes, highlighting their key features, benefits, and potential applications. We discuss the challenges associated with integrating spintronic devices into sensing/processing platforms, including power management, reliability, and design complexity. We also review various design approaches and methodologies to optimize the performance and energy efficiency of these systems. Furthermore, we explore the impact of spintronic and in-sensor processing on various application domains, such as edge computing and artificial intelligence. Finally, we discuss future research directions and potential opportunities in this emerging field, emphasizing the need for further exploration and collaboration among researchers in the areas of device technology, circuit design, and system architecture.