Multi-Objective Inverse Optimization of High-Speed Interconnects Using Cascaded Deep Neural Network

This paper proposes a novel multi-objective inverse optimization method for high-speed interconnects based on a cascaded deep neural network (DNN) structure, which can efficiently optimize characteristic impedance, insertion loss, and far-end crosstalk (FEXT) simultaneously. Parameter optimization for high-speed interconnects is essential to the signal integrity and electrical performance of complex designs such as multilayer printed circuit boards (PCBs) and chiplets. Conventional optimization approaches often rely on numerous optimization iterations, which is highly time-consuming, especially in highdimensional parameter spaces. This paper proposes a novel DNNbased method by cascading an inverse-prediction network and a forward-prediction network to achieve multi-objective optimization for characteristic impedance, insertion loss, and FEXT by optimizing the trace width, trace spacing, and pair-topair distance. Further, by incorporating an integer programming technique, parameter optimization of multilayer PCBs, including the PCB stackup and design parameters of each signal layer, can be accomplished in seconds, much more efficiently than the conventional optimization approaches.