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Integrating Artificial Neural Networks in Microwave Electronic Design Automation
Abstract
This chapter comprehensively surveys the application of artificial neural networks (ANN) in microwave circuit electronic design automation (EDA). Traditional microwave circuit EDA typically employs electromagnetic Maxwell's equations aided by numerical computation methods. However, using conventional EDA, circuit designers often need to iteratively adjust input circuit dimensions or parameters to achieve optimal performance. This chapter explores the application of AI algorithms in EDA to streamline the try-and-test design process. Incorporating AI into EDA allows for automatic optimization of circuit designs. Specifically, this chapter examines the combination of particle swarm optimization (PSO), genetic algorithms (GA) and ANN to assist in the analytical calculation of coupled microstrip lines, which are crucial for designing directional couplers and parallel-coupled filters the results are compared and validated against those obtained from commercial simulators. The history, concepts, and recent advancements of PSO, GA, ANN, and EDA are briefly described and discussed.
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