Design of the bandgap reference circuit based on deep learning

Li Hu; Jue Wang; Xianfeng Sun; Ru Yan; Yongyou Li

doi:10.1117/12.3031382

19 July 2024 Design of the bandgap reference circuit based on deep learning

Li Hu, Jue Wang, Xianfeng Sun, Ru Yan, Yongyou Li

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814C (2024) https://doi.org/10.1117/12.3031382
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

In this work, a method combining deep learning and genetic algorithm was applied to assist the design of bandgap reference circuit. Using the neural network to fit the mapping relationship between resistance values and temperature coefficient of bandgap reference circuit has significant improvement over the traditional machine learning algorithms. By using neural network as the fitness function for the genetic algorithm, the optimal design parameters of the circuit have been determined and verified. The temperature coefficient of the bandgap voltage reference is 9.7ppm after optimization.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Li Hu, Jue Wang, Xianfeng Sun, Ru Yan, and Yongyou Li "Design of the bandgap reference circuit based on deep learning", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814C (19 July 2024); https://doi.org/10.1117/12.3031382

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