Presentation
Epicell: Electro-Physical Co-Modeling for Standard Cell PPA Prediction
DescriptionIn advanced process nodes, the pursuit of extreme PPA optimization has driven an explosion in the demand for customized standard cells. To satisfy this demand, automated layout synthesis has been increasingly adopted to explore vast design spaces. However, this paradigm shifts the bottleneck from design creation to verification, as characterizing the massive volume of generated variants via SPICE is computationally prohibitive.
Meanwhile, conventional geometric heuristics fail to proxy PPA at advanced nodes due to dominant layout effects. Existing learning-based surrogates often lack the fidelity to capture these complex dependencies. To bridge this gap, we propose EPiCell, an electro-physical co-modeling framework for rapid PPA estimation. EPiCell features a Heterogeneous Graph Transformer (HGT) that explicitly models transistors, routing metals, and supply rails as distinct entities, unifying circuit topology with fine-grained layout geometry. By employing relation-aware attention, it effectively captures the non-local electro-physical interactions governing cell performance. Validated on a dataset of over 18,000 auto-generated layouts based on ASAP7, EPiCell achieves high fidelity against SPICE simulations, with low average prediction errors of 1.82% for leakage power, 4.01% for internal power, 3.06% for delay, and 3.29% for transition. Crucially, it demonstrates superior ranking consistency with SPICE, attaining a median Spearman Rank Correlation Coefficient of 0.90 for internal power, 0.81 for delay, and 0.70 for transition. This offers a scalable surrogate model to enable efficient design space exploration.
Meanwhile, conventional geometric heuristics fail to proxy PPA at advanced nodes due to dominant layout effects. Existing learning-based surrogates often lack the fidelity to capture these complex dependencies. To bridge this gap, we propose EPiCell, an electro-physical co-modeling framework for rapid PPA estimation. EPiCell features a Heterogeneous Graph Transformer (HGT) that explicitly models transistors, routing metals, and supply rails as distinct entities, unifying circuit topology with fine-grained layout geometry. By employing relation-aware attention, it effectively captures the non-local electro-physical interactions governing cell performance. Validated on a dataset of over 18,000 auto-generated layouts based on ASAP7, EPiCell achieves high fidelity against SPICE simulations, with low average prediction errors of 1.82% for leakage power, 4.01% for internal power, 3.06% for delay, and 3.29% for transition. Crucially, it demonstrates superior ranking consistency with SPICE, attaining a median Spearman Rank Correlation Coefficient of 0.90 for internal power, 0.81 for delay, and 0.70 for transition. This offers a scalable surrogate model to enable efficient design space exploration.
Event Type
Research Manuscript
TimeTuesday, July 282:21pm - 2:34pm PDT
LocationMtg Room 101B