OSA's Digital Library

Applied Spectroscopy

Applied Spectroscopy


  • Vol. 60, Iss. 10 — Oct. 1, 2006
  • pp: 1192–1197

Prediction of Surface Roughness Using X-ray Photoelectron Spectroscopy and Neural Networks

Byungwhan Kim and Min-Geun Park

Applied Spectroscopy, Vol. 60, Issue 10, pp. 1192-1197 (2006)

View Full Text Article

Acrobat PDF (189 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

  • Export Citation/Save Click for help


A new model for controlling plasma processes was constructed by combining atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS), and neural networks. The applicability of XPS to modeling etch rate was also investigated, as well as the impact of dc bias inclusion. The back-propagation neural network was used to find complex relationships between XPS and AFM data. This technique was evaluated with the etching data characterized by a 24 full factorial experiment. Five prediction models of surface roughness were constructed and compared. The Type I model refers to the model constructed with conventional process parameters. The Type II and III models were built with XPS and XPS plus dc bias data, respectively. The remaining Type IV and V models refer to those constructed with principal component analysis (PCA) reduced-XPS and PCA reduced-XPS plus dc bias, respectively. Mode prediction performance was evaluated as a function of training factor. In predicting the surface roughness, the Type II model yielded an improved prediction of 39% with respect to the Type IV model. The improvement was also demonstrated in modeling the etch rate. These results indicate that utilizing full XPS data is more effective for improving the model prediction performance. The advantage of XPS data was more conspicuous in constructing the surface roughness model.

Byungwhan Kim and Min-Geun Park, "Prediction of Surface Roughness Using X-ray Photoelectron Spectroscopy and Neural Networks," Appl. Spectrosc. 60, 1192-1197 (2006)

Sort:  Journal  |  Reset


References are not available for this paper.

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited