Artificial Intelligence based advanced Signal Integrity prediction

Abstract

As the signaling speeds continue to increase, maintaining Signal Integrity (SI) for the complete customer design space is a huge challenge. These constraints, along with the limitations of traditional methods of design space inclusion and channel behavior prediction pose significant risk to system design. Specific focus is needed on design space utilization techniques used for factoring in platform variability. Interfaces like PCIe Gen5/Gen6/Gen4 etc. exhibit higher order behaviors that can’t be modelled by current prediction algorithm like Response Surface Method (RSM). This leads to inaccurate system behavior understanding and results in unreliable platform design recommendations. To minimize design risk and achieve highly reliable scaling of Platform Design Guide (PDG) solution, this paper discusses the implementation of an Artificial Intelligence (AI) based methodology to cover complete design space and predict higher order system behaviors with high accuracy. Current SI method involves RSM type Design of Experiments (DOE) creation and results prediction using second order RSM as shown in Fig. 2(a). It has limitations since RSM uses only three variable levels therefore doesn’t cover the entire design space. It can only model up to second order system behavior. These issues can be addressed using proposed AI based methodology shown in Fig. 2(b). These AI techniques have been encapsulated into an AI based tool called Fitpro which fully automates space filled DOE creation and SI results prediction. Fitpro significantly reduces manual interventions and positively impacts efficiency.