Non-destructive thickness characterisation of 3D multilayer semiconductor devices using optical spectral measurements and machine learning

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 115
  • Download : 0
Three-dimensional (3D) semiconductor devices can address the limitations of traditional two-dimensional (2D) devices by expanding the integration space in the vertical direction. A 3D NOT-AND (NAND) flash memory device is presently the most commercially successful 3D semiconductor device. It vertically stacks more than 100 semiconductor material layers to provide more storage capacity and better energy efficiency than 2D NAND flash memory devices. In the manufacturing of 3D NAND, accurate characterisation of layer-by-layer thickness is critical to prevent the production of defective devices due to non-uniformly deposited layers. To date, electron microscopes have been used in production facilities to characterise multilayer semiconductor devices by imaging cross-sections of samples. However, this approach is not suitable for total inspection because of the wafer-cutting procedure. Here, we propose a non-destructive method for thickness characterisation of multilayer semiconductor devices using optical spectral measurements and machine learning. For > 200-layer oxide/nitride multilayer stacks, we show that each layer thickness can be non-destructively determined with an average of approximately 1.6 Å root-mean-square error. We also develop outlier detection models that can correctly classify normal and outlier devices. This is an important step towards the total inspection of ultra-high-density 3D NAND flash memory devices. It is expected to have a significant impact on the manufacturing of various multilayer and 3D devices.
Publisher
Ji Hua Laboratory
Issue Date
2021-01
Language
English
Article Type
Article
Citation

Light: Advanced Manufacturing, v.2, no.1, pp.9 - 19

ISSN
2689-9620
DOI
10.37188/lam.2021.001
URI
http://hdl.handle.net/10203/315154
Appears in Collection
ME-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0