# CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction Source: [CNN-Cap: Effective Convolutional Neural Network Based Capacitance Models for Full-Chip Parasitic Extraction]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:24:37.002Z Authors: Dingcheng Yang, Wenjian Yu, Yuanbo Guo, Wenjie Liang Published: 2021-07-14T07:14:35.000Z ## Abstract Accurate capacitance extraction is becoming more important for designing integrated circuits under advanced process technology. The pattern matching based full-chip extraction methodology delivers fast computational speed, but suffers from large error, and tedious efforts on building capacitance models of the increasing structure patterns. In this work, we propose an effective method for building convolutional neural network (CNN) based capacitance models (called CNN-Cap) for two-dimensional (2-D) structures in full-chip capacitance extraction. With a novel grid-based data representation, the proposed method is able to model the pattern with a variable number of conductors, so that largely reduce the number of patterns. Based on the ability of ResNet architecture on capturing spatial information and the proposed training skills, the obtained CNN-Cap exhibits much better performance over the multilayer perception neural network based capacitance model while being more versatile. Extensive experiments on a 55nm and a 15nm process technologies have demonstrated that the error of total capacitance produced with CNN-Cap is always within 1.3% and the error of produced coupling capacitance is less than 10% in over 99.5% probability. CNN-Cap runs more than 4000X faster than 2-D field solver on a GPU server, while it consumes negligible memory compared to the look-up table based capacitance model. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Capacitance Extraction on Synthetic data 65nm (val)](): Capacitance Extraction; Synthetic data 65nm (val); Total Error: 2.83. - [Capacitance Extraction on Real-design 65nm (test)](): Capacitance Extraction; Real-design 65nm (test); Total Error: 4.06. - [Capacitance Extraction on Synthetic data 7nm (val)](): Capacitance Extraction; Synthetic data 7nm (val); Total Error: 0.69. - [Capacitance Extraction on Real-design 7nm (test)](): Capacitance Extraction; Real-design 7nm (test); Total Error: 2.11. - [Capacitance Extraction on CNN-Cap Pattern-C 55nm (val)](): Capacitance Extraction; CNN-Cap Pattern-C 55nm (val); Error Total: 10. - [Capacitance Extraction on CNN-Cap Pattern-C 15nm (val)](): Capacitance Extraction; CNN-Cap Pattern-C 15nm (val); Total Error (Errtot): 20. ## Original sources and implementation [Original paper]() [PDF]()