We investigated the application of artificial intelligence (AI) technology for the inspection of semiconductor process equipment to address key issues such as low production efficiency and high equipment failure rates. The semiconductor industry, being central to modern technology, requires complex and precise processes where even minor defects lead to product failures, negatively impacting yield and increasing costs. Traditional inspection methods are not adequate for modern high-precision, high-efficiency production demands. By integrating advanced AI technologies, such as machine learning, deep learning, and pattern recognition, large volumes of experimental data are collected and analyzed to optimize process parameters, enhance stability, and improve product yield. By using AI, the identification and classification of defects are automated to predict potential equipment failures and reduce downtime and overall costs. By combining AI with automated optical inspection (AOI) systems, a widely used defect detection tool has been developed for semiconductor manufacturing. However, under complex conditions, AOI systems are prone to producing false positives, resulting in overkill rates above 20%. This wastes perfect products and increases the cost due to the need for manual re-inspection, hindering production efficiency. This study aims to improve wafer inspection accuracy using AI technology and reduce false alarms and overkill rates. By developing intelligent detection models, the system automatically filters out false defects and minimizes manual intervention, boosting inspection efficiency. We explored how AI is used to analyze inspection data to identify process issues and optimize workflows. The results contribute to the reduction in labor and time costs, improving equipment performance, and significantly benefitting semiconductor production management. The AI-driven method can be applied to other manufacturing processes to enhance efficiency and product quality and support the sustainable growth of the semiconductor industry.

Integrating low-carbon design into products is crucial for reducing carbon emissions throughout their life cycle and promoting sustainable development. Addressing the uncertainty in the carbon footprint resulting from the unknown choice of product material solutions. This paper considers ABC (activity-based costing) along with the components’ carbon footprint and scrap return issues to illustrate the above challenge in a two-stage production-inventory system with imperfect processes. We determine the optimal production and sales strategies that maximize total profit per unit time. An algorithm is developed to identify these optimal solutions. To illustrate the effectiveness of the proposed model and algorithm, two numerical examples from the Taiwan die casting industry are presented. Additionally, a sensitivity analysis is conducted to provide valuable managerial insights.