Generative Adversarial Network Based Adaptive Transmitter Modeling

As transmission speed keeps increasing, Serializer/Deserializer (SerDes) complexity grows increasingly with different filters, such as feed-forward equalizers (FFEs) and finite impulse response (FIR) filters. Today, vendors share information about their intellectual property (IP) through IBIS-AMI models, which requires significant engineering time. This paper presents a data-driven approach for transmitter modeling, which, coupled with prior receiver modeling work, enables end-to-end modeling of a SerDes link. The transmitter modeling considers multiple output channel conditions and different FIR configurations and recovers a pulse response for the transmitter. The recovered pulse response strongly correlates to the simulated one and has a root-mean squared error (RMSE) of 0.0080 with the data range between [0, 1]. Moreover, the results are comparable to the setup when additional image transformations are added, which should improve performance. Lastly, the proposed approach can extend the modeling to incorporate the operation at different speeds, showing the flexibility of the approach. This approach reduces design time considerably, from running simulations to the final model deployment in under a week.