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Research on Robust and Efficient Optimization Design Methods for Analog Integrated Circuits
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DOI：10.3390/mi17020184.png)
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摘要
En 中文
With the continuous evolution of CMOS technology towards deep sub-micron and advanced nodes, the challenges of analog integrated circuits (ICs) in performance, design efficiency, and reliability are increasingly prominent. The traditional design methods that rely on manual experience and repeated simulations are no longer able to meet the requirements of complex systems for high performance, high robustness, and fast iteration. In this research, the efficient and robust optimization design methods for analog ICs are systematically reviewed. Firstly, the representative efficient design methods on topology synthesis, parameter optimization, and transfer learning are studied. In addition, with the advancement of technology and the reduction of power supply voltage, parasitic effects and the influence of the external environment on circuits can no longer be ignored. Thus, the robust optimization design methods that consider process, voltage, temperature (PVT), and parasitic effects are further investigated. Then, the advantages and limitations of different methods on design efficiency, performance, and reliability are compared and discussed. Finally, an outlook on the development trend of the efficient and robust design methods for analog ICs is provided, which can provide a reference for subsequent research and engineering applications.
Keyword:
analog integrated circuits
efficient optimization design
robust optimization design
deep learning technique
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3
论文数:
1.4W
被引数:
2.9W
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