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- W4387678081 abstract "In electronic design automation (EDA), performance deviations caused by process variations need to be tackled adequately. In this paper, we present a framework to address this issue by utilizing artificial intelligence (AI) structured in a ‘grey-box’ format, which combines a ‘clear box’ representing existing circuit knowledge and ‘black box’ learning through simulation data. This framework accelerates circuit sizing over multiple process corners for a more robust design through multi-objective optimization. First, a reference solution set is selected and associated with weight variables to reformulate the original problem as a low-dimensional single-objective optimization problem. Then Bayesian optimization is used to improve the performance deviation of the circuit at different process corners for better process stability. By shifting between different process corners with high-quality solutions, the algorithmic efficiency is increased with a reduced simulation cost. Experiments show that the proposed framework outperforms an expert human designer and three state-of-the-art machine learning approaches to analog circuit sizing, offering the best results with the least circuit simulations and up to an 80% performance boost over the human design." @default.
- W4387678081 created "2023-10-17" @default.
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- W4387678081 date "2023-08-30" @default.
- W4387678081 modified "2023-10-17" @default.
- W4387678081 title "Circuit Optimization over Multiple Process Corners for Analog Electronic Design Automation" @default.
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- W4387678081 doi "https://doi.org/10.1109/icac57885.2023.10275299" @default.
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