Tackling Signal Electromigration with Learning-Based Detection and Multistage Mitigation

With the continuous scaling of integrated circuit (IC) technologies, electromigration (EM) prevails as one of the major reliability challenges facing the design of robust circuits. With such aggressive scaling in advanced technology nodes, signal nets experience high switching frequency, which further exacerbates the signal EM effect. Traditionally, signal EM fixing approaches analyze EM violations after the routing stage and repair is attempted via iterative incremental routing or cell resizing techniques. However, these "EM-analysis-then fix" approaches are ill-equipped when faced with the ever-growing EM violations in advanced technology nodes. In this work, we propose a novel signal EM handling framework that (i) incorporates EM detection and fixing techniques into earlier stages of the physical design process, and (ii) integrates machine learning based detection alongside a multistage mitigation. Experimental results demonstrate that our framework can achieve 15x speedup when compared to the state-of-the-art EDA tool while achieving similar performance in terms of EM mitigation and overhead.