This work presents a deterministic, machine-assisted framework for SI-compliant PCB design based on the Earth Mover's Distance (EMD). In contrast to conventional surrogate-based optimization methods that rely on iterative black-box search procedures, the proposed approach follows an interpretable, sequential evaluation strategy. Neural surrogate models are first used to efficiently predict waveform describing features from topology-dependent design parameters. A decision tree then acts as a physically motivated quality gate that identifies SI-compliant waveforms according to predefined SI criteria. Within the resulting valid solution space, the Earth Mover's Distance is employed as a similarity metric to rank candidate designs according to their proximity to an ideal reference signal. This enables not only the deterministic identification of admissible parameter regions but also a transparent prioritization of physically superior solutions without inverse modeling or stochastic search procedures. The methodology is demonstrated using a large-scale set of simulated DDR3 fly-by waveforms. By combining surrogate prediction, interpretable classification, and EMD-based waveform evaluation, the framework provides an explainable and computationally efficient alternative to conventional optimization strategies for supporting PCB development with AI-based methods.

翻译：本文提出一种基于推土机距离（EMD）的确定性、机器辅助框架，用于符合信号完整性（SI）要求的PCB设计。与依赖迭代黑盒搜索过程的传统代理模型优化方法不同，本方法采用可解释的序贯评估策略。首先利用神经代理模型从拓扑相关的设计参数中高效预测波形描述特征，随后决策树作为基于物理约束的质量门控，根据预定义的SI准则识别符合SI要求的波形。在所得有效解空间中，采用推土机距离作为相似性度量，依据候选设计与理想参考信号的接近程度进行排序。由此不仅能够确定性识别可行参数区域，还可无需逆建模或随机搜索过程，实现对物理上更优方案的可解释优先级排序。本方法通过大规模仿真DDR3飞控波形数据集进行验证。该框架结合代理预测、可解释分类及基于EMD的波形评估，为基于人工智能方法的PCB开发提供了较传统优化策略更具可解释性和计算效率的替代方案。