Bridging Research Gaps in AI-Enabled Electron Beam Lithography Through a Multidimensional Framework for Scalable, Sustainable, and SDG-Aligned Nanofabrication

While artificial intelligence has the potential to significantly improve optimization, precision, and process control in electron beam lithography (EBL), its practical application is still at an early stage of development. Many challenges remain before AI-driven approaches can be fully integrated into industrial lithography systems. Addressing these challenges requires collaborative and multidisciplinary research that brings together expertise from several fields, including lithography physics, control theory, machine learning, and system engineering. A deeper understanding of beam material interactions and process dynamics is necessary to develop reliable AI models, while advanced control strategies are required to maintain stability and accuracy during operation. At the same time, machine learning algorithms must be designed to be robust, adaptable, and capable of handling large and complex datasets generated during lithography processes. Effective integration of these technologies with existing fabrication systems is also essential to ensure real-time decision-making and process optimization. By combining these diverse areas of knowledge and technological development, researchers can move closer to creating dependable, scalable, and real-time AI-assisted EBL systems that are suitable for practical industrial manufacturing. In conclusion, the proposed multidimensional framework advances AI-enabled Electron Beam Lithography by addressing key research gaps while promoting sustainable nanofabrication. Through AI-driven process optimization and predictive maintenance, the framework has the potential to reduce energy consumption, material waste, and carbon emissions associated with semiconductor manufacturing. These outcomes support SDG 9 (Industry, Innovation and Infrastructure) by fostering sustainable industrial innovation, SDG 12 (Responsible Consumption and Production) through efficient resource utilization and waste reduction, and SDG 13 (Climate Action) by encouraging energy-efficient, low-carbon manufacturing practices. Future validation using sustainability metrics, such as life cycle assessment and carbon footprint analysis, will further demonstrate the framework's contribution to environmentally responsible semiconductor manufacturing.

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