Leading chipmakers are turning to machine learning to enhance electroplating and wafer cleaning processes at advanced process nodes, aiming to optimize the performance of copper interconnects. Copper, a trusted material, is facing challenges at 5nm and below due to void-free fill issues and CMP equipment limitations. As a result, engineers are exploring new methods to improve these processes.
At present, copper interconnects are primarily created using sputtering and electroplating techniques. However, with the shrinking size of CMOS devices, maintaining precision in these processes has become critical. By leveraging data from in-situ sensors and implementing ML-based algorithms, chipmakers can achieve advanced process control and enhance equipment productivity.
Specifically, copper electroplating processes are being refined to address voids and seams that impact the performance of interconnects. Chemical additives are being used to accelerate plating at the trench bottom and suppress plating on sidewalls, ensuring a more uniform fill. Monitoring of bath chemistry has also evolved, enabling real-time tracking of key parameters for better control.
On the other hand, copper CMP processes are focused on achieving planarity in metal layers through the use of chemical slurry and polishing pads. CMP pad attributes play a crucial role in ensuring efficient planarization, with strict specifications varying based on the metal layer being processed. Continuous monitoring of CMP materials, wafer properties, and equipment parameters is essential for maintaining process control and equipment health.
Overall, the adoption of machine learning and advanced process control algorithms in electroplating and CMP processes is driving improvements in yield, equipment uptime, and overall manufacturing outcomes. With the increasing complexity of CMOS nodes, the role of data-driven automation and process optimization is becoming more crucial in the semiconductor industry.
