ANSYS, Inc.
DYNAMIC CURRENT MODELING IN DYNAMIC VOLTAGE DROP ANALYSIS

Abstract:

Circuit design techniques can use a trained predictor to predict key dynamic current metrics (such as peak current, peak time, pulse width and total charge) for a gate in a circuit library, where the predictor has been trained over different combinations of different input transition slews and different output fanout models. A dynamic current model solver can be used for a gate in the cell library to derive waveforms (of current versus time) for the different combinations, and a predictor, such as a neural network, can be trained with the outputs from the solver for the different combinations. The trained predictor can be used in a runtime simulation to solve for the dynamic current demand model of the various gates in a circuit design (such as all of the gates in an integrated circuit). In one embodiment, adaptive clustering of the various instances of a gate may be used to reduce a plurality of such gates in a cluster to a representative gate that acts as a centroid instance of the gate in the cluster. Instances of the gate that drive capacitive loads through large resistive loads ("highly shielded capacitive loads") can be excluded from clustering to provide improved accuracy for these particular gates with highly shielded capacitive loads.