Chrome Extension
WeChat Mini Program
Use on ChatGLM

Precise Measurement of Thin-Film Thickness in 3D-NAND Device with CD-SEM

Journal of micro/nanolithography, MEMS, and MOEMS(2018)

Hitachi Ltd | Hitachi High Technol Corp | IMEC

Cited 6|Views39
Abstract
A method for the inline measurement of the tunnel oxide-nitride-blocking oxide (ONO) film thickness in 3D-NAND devices was studied. The ONO film, whose thickness is critical to the device properties, cannot be measured with conventional methods because it is deposited on the sidewall of a memory hole. Thus, a method to measure the thickness of this vertical film is required. We propose a critical dimension-scanning electron microscope (CD-SEM) measurement. The film thickness can be obtained by measuring the hole diameter before and after the film deposition. Namely, the decrease in the hole diameter should be twice of the thickness in principle. However, its applicability to the actual 10-nm-thick ONO film has not been verified. In this study, the measurement precision and the validity of the method were examined with actual ONO film in the 3DNAND test wafers. The results showed excellent precision (0.08 nm) and good consistency with planar transmission electron microscope (TEM) and ellipsometry results. In addition, the method revealed the subnanometer thickness difference depending on the nominal hole diameter and the hole density. It suggests the impact of inhomogeneity in the source gas supply during the film deposition. These results ensure that the method is sufficiently precise for the inline local thickness measurement of the ONO film. With this method, yield and reliability managements in the 3D-NAND device manufacturing would be improved. (C) 2018 society of Photo-Optical Instrumentation Engineers (SPIE).
More
Translated text
Key words
critical dimension-scanning electron microscope,metrology,thickness measurement,3D-NAND,oxide-nitride-blocking oxide film
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest