CML DATA

In this paper, a lightweight optimization method for complex analog integrated circuits (ICs) is proposed based on convolution neural network (CNN)-multilayer perceptron (MLP) and particle swarm optimization (PSO) algorithm. According to the circuit structure and the proposed design specifications, the circuit is divided into several sub-module circuits. Then, the sub-module and overall dataset are constructed. CNN-IC models are trained to extract the ‘transistor-circuit module-integrate circuit’ features level by level of sub-module circuits, and the global MLP models are constructed based on the established CNN-IC models. Based on the lightweight models, the parameters of analog ICs are optimized by the constrained PSO algorithm. The verification has been conducted on Cadence software, and the circuit performance meets all design specifications. Compared with ANN-opt method, the dataset and simulation time can be decreased 91% and 95%, and the optimized power consumption can be decreased 17%. Meanwhile, compared with the RL algorithm, the optimization time can be decreased 89.6%, and the optimized power consumption can be decreased 29.5% under the same dataset. The results show that the proposed lightweight optimization method can greatly decrease the dataset and simulation time, and improve the optimization efficiency of analog ICs.