Prediction of an Industrial Framework for Semiconductor Fabrication and Experimental Validation OF 300 mm Wafer

In contemporary memory semiconductor manufacturing, optimizing recipes has emerged as a crucial strategy to align with RE100 commitments, bolster ESG ratings, and substantially reduce operational costs. The current landscape demands the development of low-power manufacturing recipes that not only curtail energy consumption but also trim operational expenses, thereby augmenting overall profitability. This underscores the critical necessity for meticulous recipe optimization to ensure both sustainability and economic viability in the industry. In response to these challenges, there is an urgent need for a predictive framework capable of accurately forecasting optimal recipes. Such a framework is essential for facilitating the industry’s adaptation to dynamic technological environments and stringent environmental standards. By enabling proactive adjustments to manufacturing processes, this predictive approach ensures companies remain competitive while advancing comprehensive sustainability objectives. To address these needs, this work introduces a predictive framework specifically tailored to forecast recipe steps for plasma processes in semiconductor manufacturing. The framework’s efficacy was empirically validated using measurements from 300mm wafers in semiconductor mass production equipment. Plasma behavior was meticulously modeled, with simulations meticulously aligning with particle-in-cell models and Electron Monte Carlo collision simulations. These validations, conducted across both bulk plasma and sheath regions, confirmed the framework’s reliability and accuracy. Furthermore, the framework integrates a machine learning model to evaluate results under various recipe input conditions. This comprehensive approach not only enhances the understanding of plasma behavior but also facilitates the identification of optimal manufacturing conditions. Through meticulous recipe optimization, the framework minimizes undesirable effects such as backside chucking force and variations in thin film thickness, thereby improving overall manufacturing competitiveness and sustainability. Despite the inherent challenges and limitations, the predictive framework showcased significant potential to revolutionize manufacturing processes in the semiconductor industry. Its high predictive power positions it as a valuable tool for enhancing operational efficiency, driving informed decision-making, and ultimately advancing sustainability goals within the semiconductor manufacturing sector.