Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning

• URL: http://arxiv.org/abs/2505.10799v2
• Date: Fri, 23 May 2025 09:18:26 GMT
• Title: Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning
• Authors: Tao Bai, Junzhuo Zhou, Zeyuan Deng, Ting-Jung Lin, Wei Xing, Peng Cao, Lei He,
• Abstract summary: High accuracy requirement, large amount of data and extensive simulation cost pose severe challenges to CCS characterization.<n>We introduce a novel Gaussian Process Regression(GPR) model with active learning(AL) to establish the characterization framework efficiently and accurately.<n>Our approach significantly outperforms conventional commercial tools as well as learning based approaches by achieving an average absolute error of 2.05 ps and a relative error of 2.27% for current waveform of 57 cells under 9 process, voltage, temperature (PVT) corners with TSMC 22nm process.
• Score: 11.223940154559635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The composite current source (CCS) model has been adopted as an advanced timing model that represents the current behavior of cells for improved accuracy and better capability than traditional non-linear delay models (NLDM) to model complex dynamic effects and interactions under advanced process nodes. However, the high accuracy requirement, large amount of data and extensive simulation cost pose severe challenges to CCS characterization. To address these challenges, we introduce a novel Gaussian Process Regression(GPR) model with active learning(AL) to establish the characterization framework efficiently and accurately. Our approach significantly outperforms conventional commercial tools as well as learning based approaches by achieving an average absolute error of 2.05 ps and a relative error of 2.27% for current waveform of 57 cells under 9 process, voltage, temperature (PVT) corners with TSMC 22nm process. Additionally, our model drastically reduces the runtime to 27% and the storage by up to 19.5x compared with that required by commercial tools.

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