Presentation
Late Breaking Results: Automated Topology Generation for Power Amplifier Designs through BiLSTM-based DNN and Multi-objective Optimizations
DescriptionThis work presents an automated methodology for optimizing power amplifier (PA) design by predicting the most suitable circuit topology. Bidirectional long short-term memory (BiLSTM) deep neural network (DNN) is trained to determine the optimal PA topology, while multi-objective Pareto front optimization refines the network hyperparameters. The proposed approach is validated through high-performance PAs using lumped elements and transmission lines at a 1–2 GHz frequency range. The method is demonstrated using the Cree CGH40010 GaN HEMT on a Rogers RO4350B substrate, achieving power output of ∼40 dBm, power-added efficiency of at least 50%, and power gain exceeding 10 dB.
Event Type
Late Breaking Results
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby