摘要
Semiconductor manufacturing is an energy-intensive process with long cycle times (CTs) and high carbon emissions. This study developed a predictive optimization framework aimed at improving the operational efficiency and environmental performance of semiconductor manufacturing. In this framework, a bidirectional long short-term memory model with attention mechanisms is used to forecast CT trends, and Gauss–Newton regression is used to capture the nonlinear relationship between CT and work in progress. The CT predictions and the captured relationship are then input to a mixed-integer nonlinear programming model to generate optimal lot release strategies. In evaluation experiments, the proposed framework substantially reduced the average CT across three 12-inch wafer fabs in Taiwan. From a life-cycle perspective, shortening CT leads to lower energy usage per wafer, shorter machine idle time, and improved capacity utilization, resulting in measurable decreases in resource consumption and emissions during semiconductor manufacturing. This study contributes to the achievement of net-zero manufacturing by embedding data-driven sustainability into production planning and highlights the synergy between smart scheduling and life-cycle efficiency in semiconductor operations.