Huang X.-XChen H.-CWang S.-WJiang I.H.-RChou Y.-CTsai C.-H.HUI-RU JIANG2021-09-022021-09-02202010923152https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097922384&doi=10.1145%2f3400302.3415614&partnerID=40&md5=add5ad931cc9de550baa6bf49eaa2240https://scholars.lib.ntu.edu.tw/handle/123456789/580896Excessive dynamic IR-drop degrades the circuit performance and may lead to functional failure. Existing IR-drop fixing techniques at the placement stage do not consider the time-variant property and thus cannot handle dynamic IR-drop hotspots well. In current practice, designers perform Engineer Change Order (ECO) to move out these hotspot cells based on their experience. In this paper, we present a novel dynamic IR-drop ECO optimization and prediction framework by wise cell movement. We first spread high demand current cells in a global view to stagger their current waveforms. Then, we further move IR hotspot cells close to power/ground (PG) vias for minimizing the resistance from PG pads to their PG pins. Moreover, we propose an accurate machine learning-based dynamic IR-drop prediction model to guide the final cell movement. The features of our model capture power ground network characteristics, timing information, and cumulative current drawn by cells, thus leading to a general model applicable to ECO. Experimental results show that our proposed model precisely predicts dynamic IR-drop after cell movement, and our optimization scheme can substantially alleviate dynamic IR-drop without timing degradation. ? 2020 Association on Computer Machinery.Computer aided design; Cytology; Drops; Machine learning; Predictive analytics; Circuit performance; Current practices; Functional failure; Ground networks; Hot-spot cells; Optimization scheme; Prediction model; Timing information; Cellsconference paper10.1145/3400302.34156142-s2.0-85097922384