Dr. Shay Yogev Profile

Dr. Shay Yogev

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Proceedings Article | 27 April 2023 Poster + Presentation + Paper

Lilach Choona, Jasmine Linshiz, Shaul Pres, Boris Levant, Noam Tal, Gaetano Santoro, Sylvain Baudot, Ann Opdebeeck, Jason Reifsnider, Senthil Vadakupudhu Palayam, Gian Lorusso, Jerome Mitard, Shay Yogev

Proceedings Volume 12496, 124960Q (2023) https://doi.org/10.1117/12.2658294

KEYWORDS: Semiconducting wafers, Scanning electron microscopy, Transistors, Image processing, Etching, Performance modeling, Deep learning, Overlay metrology, Image segmentation

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In line Electrical measurement (E-Test) are the most effective predictors for EOL yield control. As technology progress with scaling, the number. of process layers increases, allowing in-line electrical measurements only after several months since lot started process in-line. As a result, each E-Test monitor controls longer and more challenging process loop. Most of the in-line pattern control that impact electrical performance measured separately for each pattern polygon and material properties. In addition, Edge Placement Error (EPE) methodology, allows combination of multiple dimensions like CD, Overlay and LER measurements to better predict yield impact. Technology shrinkage, resulting that transistor electrical performance, defined by more geomaterial parameters as well as material compositions and defectivity. In this paper we demonstrate a direct prediction from high resolution Scanning Electron Microscope (SEM) images to the first inline electrical measurement (M1) using Deep Learning (DL) techniques. The DL model provide early prediction of electrical performance, describing accurately Within Wafer (WIW) variation weeks earlier than the actual electrical measurements. Multiple layers prediction may indicate suspected process loop that modulate majority of variation and save time to solution. It can be achieved since the DL model utilizes complementary information exist on the full e-Beam image like materials and defectivity. The following results will indicate that accumulating information collected from several layers will improve prediction sensitivity and lead to even more accurate prediction capabilities. We assume that the effectiveness of the proposed prediction method will increase with process complexity, since the modulation of the existing yield predictors is losing sensitivity as design rule shrinks. In addition, since fabrication phase gets longer, the time to actual electrical measurements increase, making an early, nondestructive, and accurate prediction for electrical performance more and more valuable.

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