2024/08/23 by Haoyuan Li, Dingcheng Yang, Li, Haoyuan +5
Computer Science · Engineering · #Blind Source Separation Techniques #CCD and CMOS Imaging Sensors #FOS: Computer and information sciences #Hardware Architecture (cs.AR) #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2408.13195
openalex publication_date 2024/08/23 · openalex created_date 2024/09/21 · openalex updated_date 2026/07/28
More accurate capacitance extraction is demanded for designing integrated circuits under advanced process technology. The pattern matching approach and the field solver for capacitance extraction have the drawbacks of inaccuracy and large computational cost, respectively. Recent work \citeyang2023cnn proposes a grid-based data representation and a convolutional neural network (CNN) based capacitance models (called CNN-Cap), which opens the third way for 3-D capacitance extraction to get accurate results with much less time cost than field solver. In this work, the techniques of neural architecture search (NAS) and data augmentation are proposed to train better CNN models for 3-D capacitance extraction. Experimental results on datasets from different designs show that the obtained NAS-Cap models achieve remarkably higher accuracy than CNN-Cap, while consuming less runtime for inference and space for model storage. Meanwhile, the transferability of the NAS is validated, as the once searched architecture brought similar error reduction on coupling/total capacitance for the test cases from different design and/or process technology.