A Bidirectional Design Method for Through-Glass Vias with Selective Laser Wet Etching Based on the Cross-Modal Learning Method

TL;DR

This paper introduces a new bidirectional design method for Through-Glass Vias using machine learning to improve precision in laser etching processes.

Contribution

The novel approach combines cross-modal learning with physical models for bidirectional prediction and inverse design of TGVs.

Findings

The integration of CAED and SD models enables accurate forward prediction of TGV morphology from laser parameters.

The CLIP model supports efficient inverse design of TGVs.

The method demonstrates robustness and stability in generalization tests.

Abstract

As an interposer, Through-Glass Vias (TGVs) play a critical role in advanced packaging such as Co-packaged optics (CPO). Currently, due to the complex influence of laser wet-etching process parameters, the precise bidirectional prediction of TGV parameters and the etching morphology still remains a challenge. In this paper, a bidirectional design method for TGVs is proposed, which is based on the cross-modal learning method. By integrating a Cellular Automaton Etch-Diffusion (CAED) physical model with a Stable Diffusion (SD) architecture, accurate forward prediction from laser parameters to TGV morphology is realized successfully. In addition, the Contrastive Language–Image Pre-training (CLIP) model is also applied to achieve an efficient inverse design of TGVs. Furthermore, the generalization ability is examined in this paper, demonstrating significant robustness and stability of the generative model. The results provide an efficient method for enhancing TGV quality within a deep learning framework.

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