GAN augmented phase noise modeling of PLL for use in automated testing.

Phase noise measurement of Phase Locked Loop (PLL) in automated test equipment (ATE) is expensive and time-consuming. Indirect phase noise measurement relies on a few correlated low-cost measurements called signatures obtained from specific nodes of the PLL. A regression model using these signatures is learned during the design phase for phase noise estimation. Extraction of these signatures and phase noise performance in PLL requires significant simulation time, raising non-recurring design costs and computer aided design (CAD) tool usage costs. In this work, a new methodology for indirect phase noise measurement is developed where Generative Adversarial Network (GAN), a deep learning framework, has been used for generating a substantial amount of model-driven synthetic signatures that are strongly correlated to simulation-driven real signatures. These model-driven signatures are then used to construct a regression model for phase noise prediction. The GAN model efficacy is verified by comparing the prediction accuracy of the simulation-driven signature-based model with the GAN-augmented signature-based regression model. [ABSTRACT FROM AUTHOR]