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As the semiconductor manufacturing process is moving towards the 3 nm node, there is a crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the integrated circuit (IC) devices. EPE is the most important metric that quantify the fidelity of fabricated patterns in multi-patterning processes, and it is the combination of overlay errors and critical dimension (CD) errors. Recent advances in machine learning have enabled many new possibilities to improve the performance and efficiency of EPE optimization techniques. In this paper, we conducted a survey of recent research work that applied machine learning/ deep learning techniques for the purposes of enhancing virtual overlay metrology, reducing overlay error, and improving mask optimization methods for EPE reduction. Thorough discussions about the objectives, datasets, input features, models, key findings, and limitations are provided. In general, the results of the review work show a great potential of machine learning techniques in aiding the improvement of EPE in the field of semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

The article focuses on the properties and technological application of two-dimensional (2D) crystals such as graphene and transition metal dichalcogenides. Topics include the capacity of graphene to serve as alternative to silicon (Si)-based transistors, its application on nano-based stacks materials composed of 2D materials such as insulators, metals and semiconductors, and the challenges related to 2D materials manufacturing processes such as gate engineering, patterning, and doping.