Skip to Main Content
In this paper, a parallel automatic model generation (PAMG) technique is proposed to speedup the development of artificial neural network (ANN) models for microwave modeling. The automatic model generation (AMG) converts human based manual modeling into an automated computational process. AMG typically involves intensive computations in adaptive data sampling by repetitively driving detailed EM/physics/circuit simulators, and automatic ANN structure adaptation through iterative training stages. To improve AMG efficiency, a parallel mechanism is developed, in which the computationally intensive processes are split into smaller sections. These sections are concurrently executed on parallel processors in a multi-processor environment. The proposed parallel algorithm is formulated to maximize the number of parallel processes while minimizing the sequential overhead in the AMG to achieve the highest possible modeling efficiency. Examples of driving a physics-based device simulator for MESFET modeling and driving a circuit simulator for power amplifier behavior modeling demonstrate that the proposed PAMG dramatically shortens the model development time with parallel efficiency above 90%, thus is very useful for large-scale microwave modeling.