Electromigration (EM) has emerged as a major reliability concern for interconnects in advanced technology nodes. Most of the existing EM analysis works focus on the power lines. There exists a limited amount of work which analyzes EM failures in the signal lines. However, various emerging spintronicbased memory technologies such as the Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) and the Spin Orbit Torque Magnetic Random Access Memory (SOT-MRAM) have high current densities as compared to the conventional Static Random Access Memory (SRAM). These high current densities can lead to EM failures in the signal lines such as bit-line (BL) of these memories. Furthermore, these signal lines have workloaddependent stress as opposed to the conventional DC stress of power distribution networks. In this work, we model the EM failures in the BL of a typical STT memory array with realistic workloads. The analysis is based on physics-based EM model, which is calibrated based on industrial measurement data. The results show that the current densities in the STT arrays can be large enough to cause EM failures in the signal lines with running realistic workloads and that these failures are highly workload-dependent.
For monolithic heterogeneous integration, fast yet low-power processing and storage, and high integration density, the objective of the ED GREAT project is to co-integrate multiple digital and analog functions together within CMOS by adapting the Magnetic Tunneling Junctions (MTJ s) into a single baseline technology enabling logic, memory, and analog functions, particularly for Internet of Things (loT) platforms. This will lead to a unique STT-MTJ cell technology called Multifunctional Standardized Stack (MSS). This paper presents the progress in the project from the technology, compact modeling, process design kit, standard cells, as well as memory and system level design evaluation and exploration. The proposed technology and toolsets are giant leaps towards heterogeneous integrated technology and architectures for loT.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.