Semiconductor wafer fabrication is one of the most complex and demanding processes in industry. The process involves numerous sequential steps, including photolithography, deposition, etching, and chemical–mechanical polishing (CMP). At advanced process nodes below 5 nanometers, even angstrom-level deviations in parameters such as oxide thickness or critical dimension (CD) can lead to yield degradation or device failure. Traditional single-factor experimental methods are insufficient to capture the inherent multivariate interactions within plasma, thermal, and chemical processes. This review introduces the application of Design of Experiments (DOE) in wafer fabrication and demonstrates that it provides a statistically rigorous framework for addressing these challenges. It enables the simultaneous analysis of multiple variables, quantifying main effects and interactions, and developing predictive models with fewer runs. DOE can accelerate process development, reduce wafer consumption, enhance process robustness, and support applications in processes such as photolithography, CMP, and deposition. Beyond process optimization, DOE, combined with virtual metrology, machine learning, and digital twin technologies, provides a balanced dataset for predictive analytics and real-time control. Its functions encompass proactive monitoring, adaptive formulation optimization, and eco-efficient manufacturing aligned with sustainability goals. As wafer fabs adopt AI-assisted, simulation-driven environments, experimental design remains the foundation for knowledge-intensive, data-driven decision-making. This ensures continuous improvement in yield, manufacturability, and competitiveness in future semiconductor miniaturization processes.
Chen et al. (Fri,) studied this question.