No abstract

To meet the increasing demand for high-speed communication in VLSI (very large-scale integration) systems, next-generation highspeed data transmission standards (e.g., IEEE 802.3bs and PCIe 6.0) will adopt four-level pulse amplitude modulation (PAM-4) for data coding. Although PAM-4 is spectrally efficient to mitigate inter-symbol interference caused by bandwidth-limited wired channels, it is more sensitive than conventional non-return-to-zero line coding. To evaluate the received signal quality when using adaptive coefficient settings for a PAM-4 equalizer during data transmission, we propose an eye-opening monitor technique based on machine learning. The proposed technique uses a Gaussian mixture model to classify the received PAM-4 symbols. Simulation and experimental results demonstrate the feasibility of adaptive equalization for PAM-4 coding.

Four-level pulse amplitude modulation (PAM-4) data formats are adopted to achieve next-generation high-speed data transmission standards. In this letter, a novel eye-opening monitoring technique based on machine learning is proposed to evaluate the received signal quality for the adaptive coefficients setting of a transmitter feed-forward equalizer for PAM-4 signaling. The monitoring technique employs a Gaussian mixture model (GMM) to classify the received PAM-4 symbols. Simulation and measured results of the coefficient adjustment using the GMM method are presented.