AI Models for Supporting SI Analysis on PCB Net Structures: Comparing Linear and Non-Linear Data Sources

Abstract

Abstract. Signal integrity (SI) is an essential part in assuring the functionality of microelectronic components on a printed circuit board (PCB). Depending on the complexity of the designed interconnect structure, even the experienced PCB developer might be reliant on multiple design cycles to optimally configure the PCB parameters, which eventually results in a very complex, time-consuming and costly process. Under these aggravating conditions, artificial intelligence (AI) models may have the potential to support and simplify the SI-aware PCB design process by building predictive models and proposing design solutions to streamline the existing workflows and unburden the PCB designer. In this paper, the AI approach is divided into two separate stages consisting of neural network (NN) regression in the first step and parameterization of the PCB net structure in the second step. First, the NN models are applied to learn the relationship between the electrical parameters and the resulting signal quality captured by domain-oriented signal features in the time domain. Second, based on the trained NN models, on the one hand, the k-nearest neighbor (kNN) method is utilized to select solution candidates within the feature space, while on the other hand, genetic algorithms (GA) are applied to directly optimize the parameters of the interconnect structure. Moreover, the influence of the simulation abstraction level is investigated by comparing simulation data originating from linear and I/O buffer information specification (IBIS)-based non-linear modeling of the integrated circuit (IC) characteristics concerning the prediction accuracy and direct transferability. Finally, transfer learning concepts are evaluated to exchange learned knowledge representations between the different modeling of the IC characteristics to improve data efficiency and reduce computational complexity.