KEYWORDS: Transmission electron microscopy, Metrology, Scanning electron microscopy, Metals, 3D metrology, Molybdenum, Tin, Semiconducting wafers, Signal to noise ratio, Process control
ABSTRACT The introduction of new three-dimensional (3D) architectures in future logic and memory devices present new challenges for process metrology and control. Where there is a need to recess only one material type out of a superlattice (SL) layer stack, such structures all have in common the fact that the recess is hidden in the stack. Currently, process control in this field heavily relies on expensive, slow, and destructive metrology such as Transmission Electron Microscopy (TEM). In this work, we use the high voltage Scanning Electron Microscope (SEM) technique in combination with the Elluminator® improved Back-Scattered electrons (BSE) technology for better imaging efficiency to demonstrate the ability to measure cavity recess from top-down SEM images. We present case studies both in logic and memory domains. In Horizontal Gate-All-Around (H-GAA) Nanosheet and Forksheet logic devices, the SiGe layer is recessed in a Si/SiGe SL stack. In 3-Dimensional memory devices, we present results on Poly-Si recess metrology in Poly-Si/SiO2 SL stack, Molybdenum (Mo) recess metrology in a Mo/aSi SL stack, and TiN recess metrology in a 3DSCM stack used for memory word line. For the evaluation of the proposed recess metrology technique, several wafers with modulated recess amounts were measured using SEM technology at several high voltage landing energies (LE). BSE signal and advanced image analysis algorithms were used to build a prediction model and quantify the recessed amounts using an edge-based analysis. TEM metrology was used to validate the measurement based on the top-down high LE SEM images. We demonstrate that by using high voltage LE, in combination with enhanced BSE efficiency and advanced image analysis algorithms, we can investigate hidden layers in the stack, identify the recessed material edge, and measure accurately the cavities of interest, thus ultimately providing an inline, non-destructive, and statistically representative metrology solution for such advanced technology nodes. This new application will help chip manufacturers to characterize their processes faster and provide an HVM monitoring and control solution.
Stochastic effects in EUV patterning refer to random variations that impact local pattern edge fidelity. It can be caused by the lithography or etch processes. Distorted edge placement can result in larger pattern edge roughness, distorted pattern shape for contract holes, poor CD uniformity, and in more severe cases, partially or fully closed contacts. Large statistical SEM metrology can be used to quantify the severity of distortion and failure probability by measuring line edge roughness (LER) and line width roughness (LWR) [3].
The attempt to differentiate between normal global uniformity and local uniformity pose a metrology challenge. In this paper, we present a scanning electron microscopy (SEM) based method for detecting stochastic defects. The detected defects are reviewed by metrology and classified by defect margin merit. The proposed merit converts geometrical attributes into statistical attributes which identify whether a pattern is statistically normal or a statistical outlier.
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