GTN-Cell: Efficient Standard Cell Characterization Using Graph Transformer Network

Lookup table (LUT)-based libraries of standard cell characterization is crucial to accurate static timing analysis (STA). However, with the continuous scaling of technology nodes and the increasing complexity of circuit designs, the traditional nonlinear delay model (NLDM) is progressively unable to meet the required accuracy for cell modeling. The current source model (CSM) offers a more precise characterization of cells at advanced nodes and is able to handle arbitrary electrical waveforms. However, the CSM is highly time-consuming because it requires extensive transistor-level simulations, posing severe challenges to efficient standard cell library design. This work presents GTN-Cell, an efficient graph transformer network (GTN)-based method for library-compatible LUT-based CSM waveform prediction of standard cell characterization. GTN-Cell represents the transistor-level structures of standard cells as graphs, learning the local structural information of each cell. By incorporating the transformer encoder into the model and embedding path-related positional encodings, GTN -Cell captures the global relationships between distant nodes within each cell. Compared with HSPICE, the GTN-Cell achieves an average error of 2.27% on predicted voltage waveforms among different standard cells and timing arcs while reducing the number of simulations by 70%.