Enhanced geometrical parameters prediction in nanometrology: A hybrid metrology approach using AFM and SEM with an artificial neural network

Abstract

With the development of nanofabrication technologies, decreasing structural sizes, feature miniaturization, three-dimensional stacking, and concurrent increasing dimension characterize the measurement tasks for nano-measuring systems. Atomic Force Microscopy (AFM) and Scanning Electron Microscopy (SEM) are the most used metrology methods in nanometrology. However, each of the techniques has its inherent strengths and limitations; no single technique can provide the full capabilities, such as resolution, accuracy, and speed, to tackle the challenges of increasingly complex measurement tasks in nanometrology. In this study, a hybrid metrology approach using an Artificial Neural Network (ANN) is proposed to combine the advantages of AFM and SEM for the accurate and efficient measurements of geometrical parameters. To improve measurement efficiency, an automated measurement process utilizing deep learning has also been proposed. AFM and SEM measurement models are established to simulate training data for the ANN. This network can predict geometrical parameters more accurately with high efficiency, which can be achieved through individual techniques. Finally, the effectiveness of this method is validated by exemplary measurements for the determination of step height and pitch. This proposed approach also provides a promising solution for the laboratory-to-fab transition of metrology for semiconductors, for which automation and hybrid metrology are necessary.

Graphical abstract

Keywords

hybrid metrology / artificial neural networks / nanometrology / SEM / AFM

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