5 papers
Flash-CNNCap: Capacitance Extraction via Image Mapping
Hector R. Rodriguez, Jiechen Huang, Wenjian Yu
We present Flash-CNNCap, a CNN-based capacitance extractor that reformulates full-matrix capacitance prediction as image-to-image regression over spatial contribution maps. Prior s…
AttentionCap: Transformer Based Capacitance Matrix Learning Toward Full-Chip Extraction
Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang +3
As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacita…
CapBench: A Multi-PDK Dataset for Machine-Learning-Based Post-Layout Capacitance Extraction
Hector R. Rodriguez, Jiechen Huang, Wenjian Yu
We present CapBench, a fully reproducible, multi-PDK dataset for capacitance extraction. The dataset is derived from open-source designs, including single-core CPUs, systems-on-chi…
DeepRWCap: Neural-Guided Random-Walk Capacitance Solver for IC Design
Hector R. Rodriguez, Jiechen Huang, Wenjian Yu
Monte Carlo random walk methods are widely used in capacitance extraction for their mesh free formulation and inherent parallelism. However, modern semiconductor technologies with…
Few-shot Learning on AMS Circuits and Its Application to Parasitic Capacitance Prediction
Shan Shen, Yibin Zhang, Hector Rodriguez Rodriguez +1
Graph representation learning is a powerful method to extract features from graph-structured data, such as analog/mixed-signal (AMS) circuits. However, training deep learning model…