A Machine Learning Inspired Transceiver with ISI-Resilient Data Encoding: Hybrid-Ternary Coding + 2-Tap FFE + CTLE + Feature Extraction and Classification for 44.7dB Channel Loss in 7.3pJ/bit

Abstract

This paper presents a machine learning inspired energy-efficient transceiver targeting long-reach channels using an ISI-resilient hybrid-ternary encoding on the transmitter and feature extraction and classification on the receiver. In addition to data encoding, the proposed transceiver also employs a 2-tap FFE and CTLE to achieve communication on a 44.7dB loss FR4 channel with BER less than 1×10-6, and an energy efficiency of 7.3pJ/bit at 13.8Gb/s in 65nm CMOS.