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BP Neural Network Modeling and Solving Acceleration of Analog ICs

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Abstract

A novel accuracy-aware modeling and solving approach of adopting a back-propagation neural network (BP-NN) targeting to a classical analog five-transistor operational transconductance amplifier (OTA) is proposed in this paper. Three complex BP-NN algorithms are described amply in performance model training between multiple design factors and performance metrics, where genetic algorithm (GA), particle swarm optimization (PSO) and mind evolutionary algorithm (MEA) are introduced to further speed up the searching process for global solutions. Effectiveness comparison is performed by running simulations using SMIC 180 nm/1.8 V CMOS technology, three complex algorithms with longer time-consuming of 10 h, 30 m and 28 m demonstrate significantly superior features in solving accuracy, which show lower mean square errors (MSEs) by improvements of 82.7%, 99.7% and 99.9%, respectively. The results show that the proposed BP-NN modeling approach can be effectively aided in accelerating the global design solution of a long-cycle, nonlinear and multivariable-solved analog circuit.

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Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (NSFC, Grant No. 61704049), the Key Science and Technology Program of Henan Province (Grant No. 232102211066) and Graduate Quality Project of HAUST (Grant No. 2020ZYL-008).

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  1. Bo Liu and Weizhe Zhang have contributed equally to this work.

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  1. College of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China

    Bo Liu, Weizhe Zhang, Wenjuan Duan & Qingduan Meng

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Liu, B., Zhang, W., Duan, W. et al. BP Neural Network Modeling and Solving Acceleration of Analog ICs. Circuits Syst Signal Process 42, 7023–7044 (2023). https://doi.org/10.1007/s00034-023-02443-x

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