(85d) Efficient Cleaning Methods for Gas Supply Systems in Semiconductor Manufacture Using CFD

This study focuses on developing effective cleaning methods for specialty gas supply systems used in the semiconductor manufacturing industry. In this industry, it is crucial to maintain impurity levels within the range of parts per billion (ppb) to parts per million (ppm) for optimal manufacturing efficiency. The gas supply system comprises various components such as filters, diaphragm valves, regulators, and interconnected pipes.

Several cleaning sequences were developed to determine the most efficient cleaning method, including inert gas purging, pressurization, and high vacuum conditions facilitated by pumps. The assessment process involved considering purging time, period, and working pressure as operating variables. The objective was to reduce impurities effectively and minimize cleaning time while ensuring the safety of workers and the working environment.

In addition, the study employed Computational Fluid Dynamics (CFD) simulation to examine the hydrodynamics of residual gas in the system. This simulation enabled the visualization of dead-end zones that are difficult to detect visually. By comparing the residual gas concentration after each cleaning model, the study could determine the effectiveness of the cleaning methods in reducing impurities.

The study's results suggest a cleaning model that can minimize cleaning time and maintain the manufacturing process's efficiency. This is significant for semiconductor manufacturing industries that use specialty gas supply systems, as it provides them with valuable insights into the cleaning procedures that can help maintain impurity levels within an acceptable range.

Overall, this study highlights the importance of efficient cleaning methods for specialty gas supply systems in the semiconductor manufacturing industry. Using CFD simulation and assessing operating variables can aid in developing effective cleaning procedures that can reduce impurities, minimize cleaning time, and maintain manufacturing efficiency. The findings of this study can be useful for industry professionals and researchers alike, as they provide insights into the cleaning models that can help optimize manufacturing processes.