A Simplified Constrained Bayesian Optimization Approach to Optimize the Tx Equalization in SerDes Channels

Abstract

Design of high-speed channels has become increasingly more complicated. Due to the eye diagram closure at higher speeds, designers use Tx equalization by placing an FIR filter at Tx. Assigning the FIR tap values can be time consuming and require domain expertise since it can require sweeping hundreds or more combinations of tap values. Therefore, in this paper we propose a machine learning optimization approach to find the FIR tap values which result in the largest eye opening. Conventional optimization techniques may not be applicable in this context since specifications of the channel can require a constraint which is sum of the absolute value of the FIR taps needs to be equal to 1. Therefore, we have developed a simplified constrained Bayesian optimization approach that can automate this process and expedite calculation of the FIR tap values without requiring domain expertise. Numerical examples are provided to show efficiency of the proposed approach and compare its performance with Bayesian optimization and Genetic algorithm for this problem.