Effect of tilted wall in through glass via on the electrochemical deposition process

Glass substrates are advancing as key materials for high-density chip packaging due to their superior thermal and electrical properties. A critical challenge in through glass via (TGV) is achieving reliable copper electrodeposition via featuring tilted sidewalls. This study numerically investigates the via tilt angles and inhibitor concentrations in the electroplating process to optimize defect-free copper filling. Through finite element simulation, tilt angles ranging from 0° to 10° and varied inhibitor concentrations are investigated to identify conditions that promote butterfly-shaped copper deposition. Additionally, machine learning techniques are employed to predict the occurrence of void defects, enhancing the efficiency of the optimization process. The simulation results show that the appropriate inhibitor concentration is beneficial in balancing the deposition rate and reducing the formation of voids. Excessive tilt angle and inhibitor concentration lead to over-passivation, hindering deposition efficiency and compromising filling quality. This work provides insights into the electroplating of TGV and demonstrates the potential of machine learning to improve electroplating processes.