Transmission Channel and Matching Network Design Strategies in Chiplet-Based 2.5-D ICs for RF Applications

In this paper, a neural network-based approach for the co-design of transmission channels and matching networks in chiplet-based 2.5-D integrated circuits (ICs) for radio frequency (RF) applications is presented. This work proposes a dual-neural network framework that synergizes cascaded S-parameter modeling with data augmentation techniques. First, a cascaded method is developed to accurately model the 2.5-D signal transmission channel, incorporating through-silicon vias (TSVs), redistribution layers (RDLs), and matching networks, validated against commercial circuit tools. A convolutional neural network (CNN) is then trained to inversely map S-parameter requirements to optimal structural and matching network parameters. Concurrently, a denoising diffusion probabilistic model (DDPM) generates augmented S-parameter datasets to expand the design space while reducing simulation overhead. The framework enables automated compliance with critical constraints while optimizing secondary objectives such as matching network area minimization and frequency response enhancement. This work provides a robust artificial intelligence (AI)-driven methodology for high-efficiency RF interconnects in heterogeneous integration platforms.